In [8]:
tn.plot()
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x2733f65bf28>
In [48]:
(ddcmap.groupby('county')['classroom_teacher','principal'].median()).reset_index()
Out[48]:
county classroom_teacher principal
0 Anderson 46386.0 81234.0
1 Bedford 45733.0 78245.0
2 Benton 45908.0 64938.0
3 Bledsoe 48781.0 69205.0
4 Blount 61790.0 99120.0
5 Bradley 51357.0 86421.5
6 Campbell 45726.0 72243.0
7 Cannon 44245.0 68512.0
8 Carroll 45071.0 67514.5
9 Carter 46066.5 70638.5
10 Cheatham 45380.0 78234.0
11 Chester 45955.0 76149.0
12 Claiborne 43714.0 72503.0
13 Clay 43905.0 62520.0
14 Cocke 46372.5 75449.5
15 Coffee 50964.0 75321.0
16 Crockett 45012.0 66842.0
17 Cumberland 42485.0 73569.0
18 Davidson 51855.0 103445.0
19 Decatur 45987.0 73763.0
20 Dekalb 43183.0 67396.0
21 Dickson 44708.0 73545.0
22 Dyer 50130.5 82861.5
23 Fayette 41855.0 61612.0
24 Fentress 42922.0 63072.0
25 Franklin 46957.0 72442.0
26 Gibson 44757.0 76023.0
27 Giles 46535.0 75975.0
28 Grainger 45831.0 68502.0
29 Greene 49081.5 81274.5
30 Grundy 42582.0 63014.0
31 Hamblen 49134.0 83650.0
32 Hamilton 50469.0 90784.0
33 Hancock 41992.0 66948.0
34 Hardeman 46467.0 72536.0
35 Hardin 44760.0 63897.0
36 Hawkins 46499.5 72565.0
37 Haywood 44304.0 68504.0
38 Henderson 46787.0 70447.0
39 Henry 49238.0 81162.0
40 Hickman 44293.0 77659.0
41 Houston 47025.0 67201.0
42 Humphreys 46159.0 69350.0
43 Jackson 44936.0 68643.0
44 Jefferson 45711.0 75567.0
45 Johnson 44491.0 67080.0
46 Knox 49384.0 95880.0
47 Lake 42329.0 69334.0
48 Lauderdale 44842.0 78089.0
49 Lawrence 47480.0 73242.0
50 Lewis 47369.0 77934.5
51 Lincoln 48529.0 85181.0
52 Loudon 50248.0 86751.5
53 Macon 45804.0 74033.0
54 Madison 48908.0 84961.0
55 Marion 43847.5 65142.0
56 Marshall 48272.0 77559.0
57 Maury 47787.0 88736.0
58 Mcminn 49123.0 83138.0
59 Mcnairy 44326.0 72410.0
60 Meigs 49633.0 72803.0
61 Monroe 47785.0 70007.0
62 Montgomery 52503.0 95957.0
63 Moore 46992.0 73340.0
64 Morgan 44223.0 72545.0
65 Obion 47327.0 79783.0
66 Overton 42734.0 63263.0
67 Perry 45979.0 67719.0
68 Pickett 46202.0 65663.0
69 Polk 48429.0 77124.0
70 Putnam 47186.0 74008.0
71 Rhea 46051.0 69004.0
72 Roane 50470.0 78674.0
73 Robertson 44548.0 74412.0
74 Rutherford 51697.0 90088.5
75 Scott 44043.5 69429.0
76 Sequatchie 46463.0 73347.0
77 Sevier 50356.0 91611.0
78 Shelby 57769.0 106055.0
79 Smith 42951.0 64385.0
80 Stewart 47148.0 70787.0
81 Sullivan 52591.0 94810.0
82 Sumner 46988.0 85660.0
83 Tipton 51528.0 81081.0
84 Trousdale 44706.0 72809.0
85 Unicoi 44148.0 65544.0
86 Union 43970.0 69540.0
87 Van buren 46806.0 63002.0
88 Warren 45881.0 72458.0
89 Washington 52529.0 82704.5
90 Wayne 46135.0 70226.0
91 Weakley 44837.0 74995.0
92 White 48576.0 66789.0
93 Williamson 54144.5 109897.0
94 Wilson 50751.5 91379.5
In [358]:
freq = pct_man.pct
plt.hist(freq)
plt.ylabel('Frequency of Chronic Absenteeism Amongst TN Counties')
plt.show()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-358-674da8add515> in <module>()
      1 freq = pct_man.pct
      2 plt.hist(freq)
----> 3 plt.ylabel('Frequency of Chronic Absenteeism Amongst TN Counties')
      4 plt.show()

TypeError: 'str' object is not callable
In [359]:
plt.boxplot(freq)
ax.set_xlabel('Percentage of Chronically Absent Students')
Out[359]:
Text(0.5,325.804,'Percentage of Chronically Absent Students')
In [27]:
tn_abs1=pd.merge(group_co,tn,how = 'outer', left_on='county', right_on='NAME10')
In [ ]:
tn_abs1.shape
In [28]:
tn_abs2=pd.merge(tn_abs1,pct_man, left_on = 'NAME10', right_on='county' )
In [29]:
(tn_abs2['pct']*100).round() == tn_abs2['pct_chronically_absent'].round() # Spoke with Mary... using calculated pct may be more accurate
Out[29]:
0     False
1      True
2      True
3      True
4     False
5      True
6      True
7     False
8     False
9     False
10     True
11     True
12     True
13     True
14    False
15    False
16     True
17     True
18     True
19     True
20     True
21     True
22    False
23     True
24    False
25     True
26    False
27     True
28     True
29    False
30     True
31     True
32     True
33     True
34     True
35     True
36     True
37     True
38    False
39    False
40     True
41     True
42     True
43     True
44     True
45     True
46     True
47     True
48     True
49     True
50     True
51    False
52    False
53     True
54     True
55    False
56     True
57     True
58    False
59    False
60     True
61    False
62    False
63     True
64     True
65    False
66     True
67     True
68     True
69     True
70     True
71    False
72     True
73     True
74    False
75    False
76     True
77     True
78    False
79     True
80     True
81    False
82    False
83     True
84     True
85     True
86     True
87     True
88     True
89     True
90     True
91     True
92     True
93     True
94    False
dtype: bool
In [30]:
tn_abs2
Out[30]:
pct_chronically_absent STATEFP10 COUNTYFP10 COUNTYNS10 GEOID10 NAME10 NAMELSAD10 LSAD10 CLASSFP10 MTFCC10 CSAFP10 CBSAFP10 METDIVFP10 FUNCSTAT10 ALAND10 AWATER10 INTPTLAT10 INTPTLON10 geometry n_students n_chronically_absent pct
0 11.066667 47 001 01639722 47001 Anderson Anderson County 06 H1 G4020 314 28940 None A 873245292 19771988 +36.1167307 -084.1954177 POLYGON ((-84.33464599999999 36.0302, -84.3346... 11621 1432 0.123225
1 12.800000 47 003 01639723 47003 Bedford Bedford County 06 H1 G4020 None 43180 None A 1226710123 2983154 +35.5136604 -086.4582939 POLYGON ((-86.499674 35.359984, -86.499798 35.... 8502 1088 0.127970
2 9.000000 47 005 01639724 47005 Benton Benton County 06 H1 G4020 None None None A 1020822586 109009079 +36.0692530 -088.0712118 POLYGON ((-87.95178799999999 36.225321, -87.95... 2123 192 0.090438
3 16.600000 47 007 01639725 47007 Bledsoe Bledsoe County 06 H1 G4020 None None None A 1052635810 838365 +35.5936682 -085.2059790 POLYGON ((-85.277034 35.390432, -85.277891 35.... 1681 279 0.165973
4 7.100000 47 009 01639726 47009 Blount Blount County 06 H1 G4020 314 28940 None A 1447041818 20307831 +35.6881849 -083.9229731 POLYGON ((-84.14251999999999 35.796954, -84.14... 17779 1338 0.075257
5 11.100000 47 011 01639727 47011 Bradley Bradley County 06 H1 G4020 174 17420 None A 851489045 6959355 +35.1539144 -084.8594137 POLYGON ((-85.008478 35.09269, -85.00911099999... 15269 1704 0.111599
6 21.600000 47 013 01639728 47013 Campbell Campbell County 06 H1 G4020 314 29220 None A 1243689525 46503453 +36.4015922 -084.1592495 POLYGON ((-84.323469 36.395527, -84.3243509999... 5456 1178 0.215909
7 14.500000 47 015 01648572 47015 Cannon Cannon County 06 H1 G4020 400 34980 None A 687991703 156872 +35.8083940 -086.0624044 POLYGON ((-85.890631 35.852794, -85.8906909999... 1894 275 0.145195
8 6.833333 47 017 01639729 47017 Carroll Carroll County 06 H1 G4020 None None None A 1552055889 2144122 +35.9678963 -088.4516591 POLYGON ((-88.43844199999999 36.149538, -88.43... 4519 340 0.075238
9 19.050000 47 019 01639730 47019 Carter Carter County 06 H1 G4020 304 27740 None A 883711484 16656931 +36.2847441 -082.1265932 POLYGON ((-82.05894599999999 36.367415, -82.05... 7681 1686 0.219503
10 14.100000 47 021 01639731 47021 Cheatham Cheatham County 06 H1 G4020 400 34980 None A 783308267 11964151 +36.2551800 -087.1008163 POLYGON ((-87.148602 36.422773, -87.1486709999... 6232 881 0.141367
11 16.000000 47 023 01639732 47023 Chester Chester County 06 H1 G4020 297 27180 None A 740052043 575590 +35.4166392 -088.6055046 POLYGON ((-88.761264 35.451615, -88.7605559999... 2846 454 0.159522
12 22.900000 47 025 01639733 47025 Claiborne Claiborne County 06 H1 G4020 None None None A 1125555951 18116086 +36.5015565 -083.6607235 POLYGON ((-83.690709 36.360281, -83.691109 36.... 4219 967 0.229201
13 9.000000 47 027 01648573 47027 Clay Clay County 06 H1 G4020 None None None A 612626617 59071040 +36.5457651 -085.5457178 POLYGON ((-85.707937 36.52198, -85.70795699999... 1026 92 0.089669
14 15.100000 47 029 01639734 47029 Cocke Cocke County 06 H1 G4020 314 35460 None A 1125518964 22196995 +35.9161984 -083.1192234 POLYGON ((-83.304716 35.89886, -83.30416799999... 5127 866 0.168910
15 12.366667 47 031 01639735 47031 Coffee Coffee County 06 H1 G4020 None 46100 None A 1110993322 14496456 +35.4887586 -086.0782188 POLYGON ((-86.02704 35.343837, -86.02796599999... 9013 1138 0.126262
16 4.933333 47 033 01648574 47033 Crockett Crockett County 06 H1 G4020 None None None A 687731312 529234 +35.8113116 -089.1353494 POLYGON ((-89.08743699999999 35.694999, -89.08... 2899 136 0.046913
17 12.300000 47 035 01639736 47035 Cumberland Cumberland County 06 H1 G4020 None 18900 None A 1763847584 9825917 +35.9523984 -084.9947614 POLYGON ((-84.96183099999999 36.15048, -84.961... 7172 883 0.123118
18 16.900000 47 037 01639737 47037 Davidson Davidson County 06 H6 G4020 400 34980 None C 1305438546 56749226 +36.1691287 -086.7847898 (POLYGON ((-86.52152799999999 36.138464, -86.5... 82312 13931 0.169246
19 19.600000 47 039 01639739 47039 Decatur Decatur County 06 H1 G4020 None None None A 864654041 28588845 +35.6034221 -088.1073838 POLYGON ((-88.035213 35.616679, -88.035144 35.... 1569 307 0.195666
20 10.900000 47 041 01639738 47041 Dekalb DeKalb County 06 H1 G4020 None None None A 788253896 63816416 +35.9822204 -085.8335963 POLYGON ((-85.76769499999999 36.072922, -85.76... 2870 313 0.109059
21 10.800000 47 043 01639740 47043 Dickson Dickson County 06 H1 G4020 400 34980 None A 1268824458 3688818 +36.1455325 -087.3641546 (POLYGON ((-87.15239799999999 36.288627, -87.1... 8256 894 0.108285
22 9.750000 47 045 01639741 47045 Dyer Dyer County 06 H1 G4020 None 20540 None A 1326920403 36780572 +36.0541962 -089.3983056 POLYGON ((-89.49092499999999 35.947723, -89.49... 6334 592 0.093464
23 13.500000 47 047 01639742 47047 Fayette Fayette County 06 H1 G4020 None 32820 None A 1825386659 3762451 +35.1969933 -089.4138027 POLYGON ((-89.46137399999999 34.993857, -89.46... 3282 444 0.135283
24 13.650000 47 049 01639743 47049 Fentress Fentress County 06 H1 G4020 None None None A 1291399079 829338 +36.3760785 -084.9327026 POLYGON ((-84.73159799999999 36.350655, -84.73... 2652 347 0.130845
25 14.400000 47 051 01639744 47051 Franklin Franklin County 06 H1 G4020 None 46100 None A 1436257485 54890094 +35.1559259 -086.0992032 POLYGON ((-86.15281399999999 34.989926, -86.15... 5309 764 0.143907
26 9.880000 47 053 01639745 47053 Gibson Gibson County 06 H1 G4020 297 26480 None A 1561094821 2353199 +35.9916941 -088.9337756 POLYGON ((-88.88752199999999 35.796335, -88.88... 8757 769 0.087815
27 10.600000 47 055 01639746 47055 Giles Giles County 06 H1 G4020 None None None A 1582292854 642569 +35.2027228 -087.0353190 POLYGON ((-87.089895 34.99659, -87.09147899999... 3799 401 0.105554
28 36.200000 47 057 01648575 47057 Grainger Grainger County 06 H1 G4020 314 34100 None A 726751601 56614954 +36.2774634 -083.5094926 POLYGON ((-83.381502 36.265431, -83.3817969999... 3376 1223 0.362263
29 8.450000 47 059 01639747 47059 Greene Greene County 06 H1 G4020 None 24620 None A 1611399661 5056718 +36.1789979 -082.8477460 POLYGON ((-82.954104 35.997399, -82.954399 35.... 9606 883 0.091922
30 11.900000 47 061 01639748 47061 Grundy Grundy County 06 H1 G4020 None None None A 933778866 1522451 +35.3872730 -085.7221882 POLYGON ((-85.88787599999999 35.249037, -85.88... 2046 243 0.118768
31 6.700000 47 063 01648576 47063 Hamblen Hamblen County 06 H1 G4020 314 34100 None A 417450989 37877929 +36.2183967 -083.2660711 POLYGON ((-83.234191 36.281792, -83.2336909999... 10161 677 0.066627
32 8.900000 47 065 01639749 47065 Hamilton Hamilton County 06 H1 G4020 174 16860 None A 1404890184 86631783 +35.1591860 -085.2022955 POLYGON ((-85.17522699999999 34.985976, -85.17... 43443 3859 0.088829
33 16.500000 47 067 01648577 47067 Hancock Hancock County 06 H1 G4020 None None None A 575858117 2984311 +36.5214195 -083.2274526 POLYGON ((-83.101524 36.594039, -83.101517 36.... 972 160 0.164609
34 13.400000 47 069 01639750 47069 Hardeman Hardeman County 06 H1 G4020 None None None A 1729510170 6768412 +35.2181307 -088.9890374 POLYGON ((-89.108599 35.431897, -89.108054 35.... 3522 473 0.134299
35 15.800000 47 071 01639751 47071 Hardin Hardin County 06 H1 G4020 None None None A 1495246952 49148746 +35.2018926 -088.1856958 POLYGON ((-88.196462 35.379561, -88.196457 35.... 3433 541 0.157588
36 14.350000 47 073 01639752 47073 Hawkins Hawkins County 06 H1 G4020 304 28700 None A 1261259454 32691660 +36.4522060 -082.9313857 POLYGON ((-82.942691 36.546294, -82.9420229999... 7328 1015 0.138510
37 9.700000 47 075 01639753 47075 Haywood Haywood County 06 H1 G4020 None 15140 None A 1380753077 2457129 +35.5866898 -089.2825359 POLYGON ((-89.46887699999999 35.619034, -89.46... 2821 273 0.096774
38 12.100000 47 077 01639754 47077 Henderson Henderson County 06 H1 G4020 None None None A 1346982937 15049181 +35.6539945 -088.3876741 POLYGON ((-88.441276 35.490306, -88.441349 35.... 4689 610 0.130092
39 10.600000 47 079 01639755 47079 Henry Henry County 06 H1 G4020 None 37540 None A 1455823082 81118140 +36.3253983 -088.3003844 POLYGON ((-88.52524699999999 36.258424, -88.52... 4565 478 0.104710
40 13.100000 47 081 01639756 47081 Hickman Hickman County 06 H1 G4020 400 34980 None A 1586365395 332916 +35.8023956 -087.4671144 POLYGON ((-87.43504299999999 35.982596, -87.43... 3272 430 0.131418
41 39.400000 47 083 01648578 47083 Houston Houston County 06 H1 G4020 None None None A 518739541 17322109 +36.2857772 -087.7056048 POLYGON ((-87.79108599999999 36.244559, -87.79... 1329 523 0.393529
42 14.400000 47 085 01639757 47085 Humphreys Humphreys County 06 H1 G4020 None None None A 1375232049 66620771 +36.0404396 -087.7906251 POLYGON ((-87.707509 36.227205, -87.7074119999... 2842 410 0.144265
43 9.900000 47 087 01639758 47087 Jackson Jackson County 06 H1 G4020 None 18260 None A 798545703 29019623 +36.3542420 -085.6741819 POLYGON ((-85.60680599999999 36.479111, -85.60... 1444 143 0.099030
44 13.200000 47 089 01639759 47089 Jefferson Jefferson County 06 H1 G4020 314 34100 None A 709858548 104186904 +36.0484787 -083.4409664 POLYGON ((-83.490697 36.166588, -83.489797 36.... 7105 938 0.132020
45 8.700000 47 091 01639760 47091 Johnson Johnson County 06 H1 G4020 None None None A 773046157 11001626 +36.4532035 -081.8612367 POLYGON ((-81.993505 36.433888, -81.993904 36.... 1984 173 0.087198
46 14.900000 47 093 01639761 47093 Knox Knox County 06 H1 G4020 314 28940 None A 1316271254 45715161 +35.9927265 -083.9377209 POLYGON ((-84.072906 36.049719, -84.0730239999... 58691 8767 0.149376
47 19.900000 47 095 01639762 47095 Lake Lake County 06 H1 G4020 None None None A 429379130 72802129 +36.3339054 -089.4855365 POLYGON ((-89.537183 36.274385, -89.5376749999... 772 154 0.199482
48 9.800000 47 097 01639763 47097 Lauderdale Lauderdale County 06 H1 G4020 None None None A 1222453808 92173871 +35.7629507 -089.6277318 POLYGON ((-89.57808899999999 35.925089, -89.57... 4045 398 0.098393
49 8.300000 47 099 01639764 47099 Lawrence Lawrence County 06 H1 G4020 None 29980 None A 1598355330 2229584 +35.2204764 -087.3965460 POLYGON ((-87.437074 35.002776, -87.438316 35.... 6653 554 0.083271
50 11.000000 47 101 01639765 47101 Lewis Lewis County 06 H1 G4020 None None None A 730608109 1031587 +35.5232441 -087.4969827 POLYGON ((-87.32755399999999 35.5686, -87.3275... 3024 331 0.109458
51 13.500000 47 103 01639766 47103 Lincoln Lincoln County 06 H1 G4020 None None None A 1477169515 990366 +35.1425322 -086.5933882 POLYGON ((-86.83367799999999 35.082324, -86.83... 3767 507 0.134590
52 13.100000 47 105 01639767 47105 Loudon Loudon County 06 H1 G4020 314 28940 None A 593665426 47019542 +35.7374500 -084.3162040 (POLYGON ((-84.539963 35.670465, -84.540218999... 6869 818 0.119086
53 10.800000 47 111 01639768 47111 Macon Macon County 06 H1 G4020 400 34980 None A 795498380 246691 +36.5378383 -086.0012306 POLYGON ((-85.94728099999999 36.420861, -85.94... 3828 415 0.108412
54 21.500000 47 113 01639769 47113 Madison Madison County 06 H1 G4020 297 27180 None A 1442926509 3908964 +35.6060563 -088.8334238 POLYGON ((-88.787847 35.793249, -88.7868259999... 12480 2688 0.215385
55 24.500000 47 115 01639770 47115 Marion Marion County 06 H1 G4020 174 16860 None A 1290227262 36501967 +35.1334215 -085.6183990 POLYGON ((-85.55271399999999 34.983996, -85.55... 4245 828 0.195053
56 15.300000 47 117 01639771 47117 Marshall Marshall County 06 H1 G4020 None 30280 None A 972437127 1801806 +35.4683866 -086.7658862 POLYGON ((-86.790753 35.703708, -86.7892689999... 5341 818 0.153155
57 12.600000 47 119 01639772 47119 Maury Maury County 06 H1 G4020 400 17940 None A 1588021250 6319194 +35.6156963 -087.0777632 POLYGON ((-87.160281 35.435369, -87.1636839999... 12228 1535 0.125532
58 13.200000 47 107 01639773 47107 Mcminn McMinn County 06 H1 G4020 174 11940 None A 1114018337 5331264 +35.4244708 -084.6199625 POLYGON ((-84.736514 35.516316, -84.7355879999... 7416 1025 0.138215
59 9.500000 47 109 01639774 47109 Mcnairy McNairy County 06 H1 G4020 None None None A 1457801922 1947871 +35.1756263 -088.5646713 POLYGON ((-88.716481 35.256343, -88.7164739999... 4124 390 0.094568
60 12.000000 47 121 01639775 47121 Meigs Meigs County 06 H1 G4020 None None None A 505362806 56013385 +35.5033973 -084.8238876 POLYGON ((-84.81092199999999 35.436491, -84.81... 1663 199 0.119663
61 14.250000 47 123 01639776 47123 Monroe Monroe County 06 H1 G4020 None None None A 1646104693 44013070 +35.4476659 -084.2497859 POLYGON ((-84.450047 35.472714, -84.4512189999... 6787 1011 0.148961
62 8.500000 47 125 01639777 47125 Montgomery Montgomery County 06 H1 G4020 None 17300 None A 1396461566 12050596 +36.5003535 -087.3808865 POLYGON ((-87.564016 36.339567, -87.564296 36.... 33026 2819 0.085357
63 13.600000 47 127 01648579 47127 Moore Moore County 06 H6 G4020 None 46100 None C 334684959 3093426 +35.2888885 -086.3586840 POLYGON ((-86.444197 35.277975, -86.4441159999... 839 114 0.135876
64 8.800000 47 129 01639778 47129 Morgan Morgan County 06 H1 G4020 None None None A 1352440400 809723 +36.1386970 -084.6392616 POLYGON ((-84.42549099999999 36.123657, -84.42... 2981 261 0.087555
65 12.250000 47 131 01639779 47131 Obion Obion County 06 H1 G4020 542 46460 None A 1410839429 27916074 +36.3581749 -089.1501746 POLYGON ((-89.256345 36.506316, -89.250058 36.... 4909 657 0.133836
66 11.000000 47 133 01639780 47133 Overton Overton County 06 H1 G4020 None 18260 None A 1122714642 3500016 +36.3448504 -085.2830756 POLYGON ((-85.326746 36.2009, -85.326901999999... 3014 333 0.110484
67 11.000000 47 135 01639781 47135 Perry Perry County 06 H1 G4020 None None None A 1074148474 21084076 +35.6597850 -087.8770272 POLYGON ((-87.981763 35.774973, -87.9817 35.77... 1039 114 0.109721
68 15.600000 47 137 01648580 47137 Pickett Pickett County 06 H1 G4020 None None None A 422113383 29822854 +36.5593638 -085.0757410 POLYGON ((-85.17018 36.62484, -85.168763 36.62... 693 108 0.155844
69 14.700000 47 139 01639782 47139 Polk Polk County 06 H1 G4020 174 17420 None A 1125806416 19834205 +35.1094371 -084.5411124 POLYGON ((-84.490274 35.283502, -84.488781 35.... 2299 338 0.147020
70 10.000000 47 141 01639783 47141 Putnam Putnam County 06 H1 G4020 None 18260 None A 1038852585 3784351 +36.1408072 -085.4969279 POLYGON ((-85.39535599999999 36.055085, -85.39... 10948 1098 0.100292
71 11.600000 47 143 01639784 47143 Rhea Rhea County 06 H1 G4020 None None None A 816822960 54463425 +35.6005870 -084.9495522 POLYGON ((-85.087121 35.587647, -85.086478 35.... 5089 712 0.139910
72 13.000000 47 145 01639785 47145 Roane Roane County 06 H1 G4020 314 25340 None A 934229744 88755772 +35.8474721 -084.5238612 POLYGON ((-84.620887 35.932199, -84.620812 35.... 6517 846 0.129814
73 7.400000 47 147 01639786 47147 Robertson Robertson County 06 H1 G4020 400 34980 None A 1233577495 451694 +36.5275304 -086.8693774 POLYGON ((-87.073559 36.427653, -87.07508 36.4... 11149 828 0.074267
74 8.700000 47 149 01639787 47149 Rutherford Rutherford County 06 H1 G4020 400 34980 None A 1604145428 12143221 +35.8433688 -086.4172127 POLYGON ((-86.607743 35.981573, -86.6077279999... 51769 5237 0.101161
75 8.650000 47 151 01639788 47151 Scott Scott County 06 H1 G4020 None None None A 1378642788 2414259 +36.4372392 -084.4983861 POLYGON ((-84.520921 36.596423, -84.501395 36.... 3965 301 0.075914
76 19.200000 47 153 01652643 47153 Sequatchie Sequatchie County 06 H1 G4020 174 16860 None A 688567749 481448 +35.3723348 -085.4103438 POLYGON ((-85.44302599999999 35.26185, -85.443... 2204 424 0.192377
77 20.600000 47 155 01639789 47155 Sevier Sevier County 06 H1 G4020 314 42940 None A 1534567408 13514945 +35.7912836 -083.5219545 POLYGON ((-83.784379 35.876163, -83.7843819999... 14373 2956 0.205663
78 8.500000 47 157 01639790 47157 Shelby Shelby County 06 H1 G4020 None 32820 None A 1976612450 56576401 +35.1837942 -089.8953970 POLYGON ((-90.137647 34.994714, -90.1376509999... 137300 20038 0.145943
79 5.000000 47 159 01639791 47159 Smith Smith County 06 H1 G4020 400 34980 None A 814004493 28706391 +36.2556502 -085.9420775 POLYGON ((-86.095461 36.34193399999999, -86.09... 2991 150 0.050150
80 15.100000 47 161 01639792 47161 Stewart Stewart County 06 H1 G4020 None 17300 None A 1189659929 87232255 +36.5117556 -087.8515483 POLYGON ((-87.84630899999999 36.328947, -87.84... 1983 300 0.151286
81 14.466667 47 163 01639793 47163 Sullivan Sullivan County 06 H1 G4020 304 28700 None A 1070605281 42330294 +36.5102123 -082.2993965 POLYGON ((-82.51727099999999 36.595391, -82.51... 20940 3163 0.151051
82 9.500000 47 165 01639794 47165 Sumner Sumner County 06 H1 G4020 400 34980 None A 1371267673 35658276 +36.4700152 -086.4585173 POLYGON ((-86.68059799999999 36.315954, -86.68... 28914 2739 0.094729
83 9.700000 47 167 01639795 47167 Tipton Tipton County 06 H1 G4020 None 32820 None A 1187161824 38714600 +35.5002966 -089.7637081 (POLYGON ((-90.06520999999999 35.418215, -90.0... 10749 1048 0.097497
84 10.200000 47 169 01648581 47169 Trousdale Trousdale County 06 H6 G4020 400 34980 None C 295758787 6345335 +36.3930297 -086.1566909 POLYGON ((-86.230096 36.349172, -86.235117 36.... 1259 129 0.102462
85 20.800000 47 171 01648582 47171 Unicoi Unicoi County 06 H1 G4020 304 27740 None A 482165202 850686 +36.1002148 -082.4182453 POLYGON ((-82.32576399999999 36.119001, -82.32... 2279 474 0.207986
86 17.400000 47 173 01648583 47173 Union Union County 06 H1 G4020 314 28940 None A 578990181 61092965 +36.2841401 -083.8360878 POLYGON ((-83.98571699999999 36.281478, -83.98... 3517 611 0.173728
87 10.100000 47 175 01648584 47175 Van buren Van Buren County 06 H1 G4020 None None None A 708142475 2893424 +35.6992446 -085.4584114 POLYGON ((-85.59742199999999 35.726036, -85.59... 701 71 0.101284
88 14.200000 47 177 01639796 47177 Warren Warren County 06 H1 G4020 None 32660 None A 1120635730 3508343 +35.6782817 -085.7773633 POLYGON ((-85.81362399999999 35.845816, -85.81... 6469 920 0.142217
89 13.600000 47 179 01639797 47179 Washington Washington County 06 H1 G4020 304 27740 None A 845539916 8632655 +36.2956649 -082.4950374 POLYGON ((-82.523303 36.434275, -82.5230299999... 16232 2230 0.137383
90 17.300000 47 181 01639798 47181 Wayne Wayne County 06 H1 G4020 None None None A 1901310603 4023299 +35.2426873 -087.8197026 POLYGON ((-87.981697 35.28794, -87.981695 35.2... 2141 370 0.172816
91 11.900000 47 183 01639799 47183 Weakley Weakley County 06 H1 G4020 542 32280 None A 1503135455 3679548 +36.3035229 -088.7207846 POLYGON ((-88.94070000000001 36.203893, -88.94... 4136 493 0.119197
92 9.000000 47 185 01639800 47185 White White County 06 H1 G4020 None None None A 975578971 7128063 +35.9270621 -085.4557657 (POLYGON ((-85.25227199999999 35.770612, -85.2... 3864 349 0.090321
93 5.600000 47 187 01639801 47187 Williamson Williamson County 06 H1 G4020 400 34980 None A 1508925443 3028823 +35.8949720 -086.8969580 POLYGON ((-86.832753 36.041024, -86.832499 36.... 41203 2600 0.063102
94 9.500000 47 189 01639802 47189 Wilson Wilson County 06 H1 G4020 400 34980 None A 1478433235 32037460 +36.1484757 -086.2902097 POLYGON ((-86.442217 36.057074, -86.4435759999... 21242 2355 0.110865
In [35]:
fig, ax = plt.subplots(1, figsize=(20, 12))
merged.plot(column='pct', cmap='Blues', linewidth=0.8, ax=ax, edgecolor='0.8')
ax.axis('off')
ax.set_title('Percentage of Chronically Absent Students in Tennessee', fontdict={'fontsize': '25', 'fontweight' : '3'})

# create an annotation for the data source

ax.annotate('Source: U.S. Census Bureau, 2010 & TN Dept. of Education 2017',xy=(0.1, .08),  xycoords= 'figure fraction', horizontalalignment='left', verticalalignment='top', fontsize=12, color= '#555555')

# Create colorbar as a legend

sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=0, vmax=1))

# empty array for the data range

sm._A = []

# add the colorbar to the figure

cbar = fig.colorbar(sm)
In [200]:
from bokeh.io import output_notebook, show, output_file
from bokeh.plotting import figure
from bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar
from bokeh.palettes import brewer
from bokeh.io import curdoc, output_notebook
from bokeh.models import Slider, HoverTool
from bokeh.layouts import widgetbox, row, column


#Input GeoJSON source that contains features for plotting.
geosource = GeoJSONDataSource(geojson = json_data)

#Define a sequential multi-hue color palette.
palette = brewer['YlGnBu'][8]

#Reverse color order so that dark blue is highest percentage.???
palette = palette[::-1]

#Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors.
color_mapper = LinearColorMapper(palette = palette, low = 0, high = 1)

#Define custom tick labels for color bar.
tick_labels = {'0': '0%', '5': '5%', '10':'10%', '15':'15%', '20':'20%', '25':'25%', '30':'30%','35':'35%', '40': '>40%'}

#Create color bar. 
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
border_line_color=None,location = (0,0), orientation = 'horizontal', major_label_overrides = tick_labels)

#Create figure object.
#p = figure(title = 'Percentage of Students Chronically Absent, 2016-2017', plot_height = 400 , plot_width = 950, toolbar_location = None)
#p.xgrid.grid_line_color = None
#p.ygrid.grid_line_color = None

#Add hover tool
hover = HoverTool(tooltips = [ ('County','@NAMELSAD10'),('% chronically absent','@pct')])

#Create figure object.
p = figure(title = 'Percentage of Chronically Absent Students, 2016-2017', plot_height = 400 , plot_width = 800, toolbar_location = None, tools = [hover])
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None

#Add patch renderer to figure. 
p.patches('xs','ys', source = geosource,fill_color = {'field' :'pct', 'transform' : color_mapper},
          line_color = 'black', line_width = 0.25, fill_alpha = 1)

#Specify figure layout.
p.add_layout(color_bar, 'below')

#Display figure inline in Jupyter Notebook.
output_notebook()

#Display figure.
show(p)
Loading BokehJS ...
In [49]:
salary_tp=ddcmap.groupby('county')['classroom_teacher','principal'].median() #Median... attempted to avoid extreme outliers if present
In [50]:
salary_tp.head()
Out[50]:
classroom_teacher principal
county
Anderson 46386.0 81234.0
Bedford 45733.0 78245.0
Benton 45908.0 64938.0
Bledsoe 48781.0 69205.0
Blount 61790.0 99120.0
In [51]:
tn_as=pd.merge(tn_abs2,salary_tp, left_on = 'NAME10', right_on='county') #tn_absence,salary... salary is based on median to avoid outliers
In [53]:
tn_as1=tn_as.iloc[:,[5, 19,20,21,22,23]]
In [55]:
health17tn = pd.read_csv('data/Outcomes and Factor Rankings Data 1719.csv')
In [56]:
health17tn.columns = ('FIPS', 'State', 'County', 'Health Outcomes Z-score', 'Health Outcomes Rank', 'Health Factors Z-score', 'Health Factors Rank', '')
In [ ]:
health17tn
In [57]:
health17subranks = pd.read_csv('data/Outcomes and Factor Subrankings Data 1719.csv')
In [62]:
health17subranks.columns = ('FIPS', 'State', 'County', 'Length of Life Z-score', 'Length of Life Rank', ' Quality of Life Z-score', 'Quality of Life Rank', 'Health Behaviors Z-Score', 'Health Behaviors Rank', ' Clinical Care Z-score', 'Clinical Care Rank', 'Social & Economic Factors Z-score', 'Social & Economic Factors Rank', 'Physical Environment Z-score', 'Physical Environment Rank', '')
In [ ]:
health17subranks.head()
In [59]:
disparaties17 = pd.read_csv('data/Ranked Measure Data 1719.csv')
more_disparaties17 = pd.read_csv('data/Additional Data Measures 1719.csv')
In [60]:
disparaties17['Unnamed: 2'] = disparaties17['Unnamed: 2'].str.capitalize()
In [69]:
disparaties17.head()
Out[69]:
Unnamed: 0 Unnamed: 1 Unnamed: 2 Premature death Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9 Poor or fair health Unnamed: 11 Unnamed: 12 Unnamed: 13 Poor physical health days Unnamed: 15 Unnamed: 16 Unnamed: 17 Poor mental health days Unnamed: 19 Unnamed: 20 Unnamed: 21 Low birthweight Unnamed: 23 Unnamed: 24 Unnamed: 25 Unnamed: 26 Unnamed: 27 Unnamed: 28 Unnamed: 29 Adult smoking Unnamed: 31 Unnamed: 32 Unnamed: 33 Adult obesity Unnamed: 35 Unnamed: 36 Unnamed: 37 Food environment index Unnamed: 39 Physical inactivity Unnamed: 41 Unnamed: 42 Unnamed: 43 Access to exercise opportunities Unnamed: 45 Excessive drinking Unnamed: 47 Unnamed: 48 Unnamed: 49 Alcohol-impaired driving deaths Unnamed: 51 Unnamed: 52 Unnamed: 53 Unnamed: 54 Unnamed: 55 Sexually transmitted infections Unnamed: 57 Unnamed: 58 Teen births Unnamed: 60 Unnamed: 61 Unnamed: 62 Unnamed: 63 Unnamed: 64 Unnamed: 65 Uninsured Unnamed: 67 Unnamed: 68 Unnamed: 69 Unnamed: 70 Primary care physicians Unnamed: 72 Unnamed: 73 Unnamed: 74 Dentists Unnamed: 76 Unnamed: 77 Unnamed: 78 Mental health providers Unnamed: 80 Unnamed: 81 Unnamed: 82 Preventable hospital stays Unnamed: 84 Unnamed: 85 Unnamed: 86 Unnamed: 87 Mammography screening Unnamed: 89 Unnamed: 90 Unnamed: 91 Unnamed: 92 Flu vaccinations Unnamed: 94 Unnamed: 95 Unnamed: 96 Unnamed: 97 High school graduation Unnamed: 99 Unnamed: 100 Some college Unnamed: 102 Unnamed: 103 Unnamed: 104 Unnamed: 105 Unnamed: 106 Unemployment Unnamed: 108 Unnamed: 109 Unnamed: 110 Children in poverty Unnamed: 112 Unnamed: 113 Unnamed: 114 Unnamed: 115 Unnamed: 116 Unnamed: 117 Income inequality Unnamed: 119 Unnamed: 120 Unnamed: 121 Children in single-parent households Unnamed: 123 Unnamed: 124 Unnamed: 125 Unnamed: 126 Unnamed: 127 Social associations Unnamed: 129 Unnamed: 130 Violent crime Unnamed: 132 Unnamed: 133 Injury deaths Unnamed: 135 Unnamed: 136 Unnamed: 137 Unnamed: 138 Air pollution - particulate matter Unnamed: 140 Drinking water violations Unnamed: 142 Severe housing problems Unnamed: 144 Unnamed: 145 Unnamed: 146 Unnamed: 147 Unnamed: 148 Unnamed: 149 Driving alone to work Unnamed: 151 Unnamed: 152 Unnamed: 153 Unnamed: 154 Unnamed: 155 Unnamed: 156 Long commute - driving alone Unnamed: 158 Unnamed: 159 Unnamed: 160 Unnamed: 161
0 FIPS State County Years of Potential Life Lost Rate 95% CI - Low 95% CI - High Z-Score YPLL Rate (Black) YPLL Rate (Hispanic) YPLL Rate (White) % Fair/Poor 95% CI - Low 95% CI - High Z-Score Physically Unhealthy Days 95% CI - Low 95% CI - High Z-Score Mentally Unhealthy Days 95% CI - Low 95% CI - High Z-Score Unreliable % LBW 95% CI - Low 95% CI - High Z-Score % LBW (Black) % LBW (Hispanic) % LBW (White) % Smokers 95% CI - Low 95% CI - High Z-Score % Obese 95% CI - Low 95% CI - High Z-Score Food Environment Index Z-Score % Physically Inactive 95% CI - Low 95% CI - High Z-Score % With Access Z-Score % Excessive Drinking 95% CI - Low 95% CI - High Z-Score # Alcohol-Impaired Driving Deaths # Driving Deaths % Alcohol-Impaired 95% CI - Low 95% CI - High Z-Score # Chlamydia Cases Chlamydia Rate Z-Score Teen Birth Rate 95% CI - Low 95% CI - High Z-Score Teen Birth Rate (Black) Teen Birth Rate (Hispanic) Teen Birth Rate (White) # Uninsured % Uninsured 95% CI - Low 95% CI - High Z-Score # Primary Care Physicians PCP Rate PCP Ratio Z-Score # Dentists Dentist Rate Dentist Ratio Z-Score # Mental Health Providers MHP Rate MHP Ratio Z-Score Preventable Hosp. Rate Z-Score Preventable Hosp. Rate (Black) Preventable Hosp. Rate (Hispanic) Preventable Hosp. Rate (White) % Screened Z-Score % Screened (Black) % Screened (Hispanic) % Screened (White) % Vaccinated Z-Score % Vaccinated (Black) % Vaccinated (Hispanic) % Vaccinated (White) Cohort Size Graduation Rate Z-Score # Some College Population % Some College 95% CI - Low 95% CI - High Z-Score # Unemployed Labor Force % Unemployed Z-Score % Children in Poverty 95% CI - Low 95% CI - High Z-Score % Children in Poverty (Black) % Children in Poverty (Hispanic) % Children in Poverty (White) 80th Percentile Income 20th Percentile Income Income Ratio Z-Score # Single-Parent Households # Households % Single-Parent Households 95% CI - Low 95% CI - High Z-Score # Associations Association Rate Z-Score Annual Average Violent Crimes Violent Crime Rate Z-Score # Injury Deaths Injury Death Rate 95% CI - Low 95% CI - High Z-Score Average Daily PM2.5 Z-Score Presence of violation Z-Score % Severe Housing Problems 95% CI - Low 95% CI - High Severe Housing Cost Burden Overcrowding Inadequate Facilities Z-Score % Drive Alone 95% CI - Low 95% CI - High Z-Score % Drive Alone (Black) % Drive Alone (Hispanic) % Drive Alone (White) # Workers who Drive Alone % Long Commute - Drives Alone 95% CI - Low 95% CI - High Z-Score
1 47000 Tennessee NaN 9121 9039 9204 NaN NaN NaN NaN 19 18 20 NaN 4.4 4.1 4.7 NaN 4.5 4.1 4.8 NaN NaN 9 9 9 NaN NaN NaN NaN 22 21 24 NaN 33 NaN NaN NaN 6.3 NaN 27 NaN NaN NaN 71 NaN 14 13 16 NaN 1311 4997 26 26 27 NaN 32304 489.4 NaN 33 33 34 NaN NaN NaN NaN 581927 11 10 11 NaN 4802 72 1385:1 NaN 3574 53 1879:1 NaN 9531 142 705:1 NaN 5305 NaN NaN NaN NaN 40 NaN NaN NaN NaN 48 NaN NaN NaN NaN 71587 90 NaN 1033412 1716447 60 60 61 NaN 118560 3198773 3.7 NaN 21 20 22 NaN NaN NaN NaN 97093 20480 4.7 NaN 525915 1483144 35 35 36 NaN 7505 11.3 NaN 40973 621 NaN 28245 86 85 87 NaN 10.0 NaN NaN NaN 15 15 15 NaN NaN NaN NaN 84 83 84 NaN NaN NaN NaN 2466424 34 34 35 NaN
2 47001 Tennessee Anderson 9536 8703 10368 -0.40 11586 NaN 9749 19 18 20 -0.51 4.7 4.5 4.9 -0.47 4.7 4.5 4.9 -0.35 NaN 9 8 10 -0.10 12 NaN 9 21 20 22 -0.68 32 26 38 -0.56 7.5 0.23 28 23 34 -0.41 66 -0.62 14 13 14 0.00 18 60 30 23 37 0.14 263 347.2 -0.09 37 34 40 -0.19 29 24 38 5550 9 8 11 -1.31 46 61 1651:1 -0.49 55 72 1386:1 -2.03 69 90 1105:1 -0.26 4867 -0.58 5397 NaN 4825 49 -1.98 53 54 49 54 -1.49 42 43 55 859 92 0.15 10273 17614 58 54 63 -0.98 1333 34175 3.9 -0.51 22 16 28 -0.35 26 34 25 96851 19940 4.9 0.62 5894 15656 38 32 43 0.65 118 15.5 -1.22 251 401 0.05 404 107 96 117 0.46 10.3 0.55 No -0.59 13 12 15 12 1 1 -0.08 87 85 89 0.77 95 77 90 27323 34 31 37 -0.42
3 47003 Tennessee Bedford 9762 8769 10754 -0.28 9265 4454 10683 22 21 22 0.45 4.8 4.6 5.0 -0.10 4.7 4.5 4.9 -0.20 NaN 9 8 10 -0.11 14 6 9 21 21 22 -0.36 33 26 39 -0.30 7.9 -0.38 33 26 39 0.79 51 0.13 15 14 15 0.68 24 47 51 45 57 2.08 153 324.3 -0.20 43 39 47 0.42 33 49 43 5173 13 11 15 1.19 18 38 2638:1 0.27 12 25 4010:1 0.57 33 69 1458:1 0.04 6034 0.09 3236 NaN 6300 42 -0.65 51 29 41 45 0.10 36 21 45 571 91 0.45 5013 12114 41 36 46 0.64 804 20539 3.9 -0.49 23 17 29 -0.19 29 29 19 82588 21083 3.9 -1.24 4316 11929 36 30 42 0.44 45 9.5 0.36 200 425 0.17 203 86 74 98 -0.50 10.5 0.95 Yes 1.66 15 13 18 12 4 1 0.72 82 79 85 -0.74 81 64 81 17179 36 31 40 -0.25
4 47005 Tennessee Benton 12828 10692 14965 1.40 NaN NaN NaN 21 20 22 0.32 5.2 5.0 5.5 1.07 5.1 4.9 5.4 1.34 NaN 8 7 10 -0.63 NaN NaN NaN 23 22 24 0.24 35 28 41 0.54 7.4 0.38 32 25 39 0.52 48 0.27 12 12 13 -1.23 11 27 41 31 50 1.13 56 347.2 -0.09 50 42 58 1.15 NaN NaN NaN 1431 12 10 13 0.41 3 19 5338:1 0.91 3 19 5329:1 0.91 6 38 2664:1 0.48 6528 0.37 NaN NaN NaN 32 1.25 NaN NaN NaN 47 -0.25 37 40 48 166 96 -0.77 1515 3422 44 36 52 0.36 364 6769 5.4 1.28 30 21 39 0.91 NaN 93 28 69035 14105 4.9 0.69 1152 3209 36 25 47 0.40 21 13.1 -0.59 25 152 -1.15 130 161 134 189 3.00 10.0 -0.06 No -0.59 16 13 19 13 3 1 1.07 87 83 91 0.83 NaN NaN NaN 4807 27 22 32 -1.04
In [ ]:
disparaties179 = disparaties17
In [ ]:
disparaties179.columns = disparaties17.iloc[0,:]
In [ ]:
disparaties179.head()
In [63]:
more_disparaties17['Unnamed: 2'] = more_disparaties17['Unnamed: 2'].str.capitalize()
health17tn['County'] = health17tn['County'].str.capitalize()
health17subranks['County'] = health17subranks['County'].str.capitalize()
In [64]:
abs_health=pd.merge(tn_as1,health17tn, left_on = 'NAME10', right_on='County')
In [132]:
abs_health=abs_health.iloc[:,[0,1,2,3,4,5,9,10,11,12,13]]
In [140]:
abs_health.columns
Out[140]:
Index(['NAME10', 'n_students', 'n_chronically_absent', 'pct', 'classroom_teacher', 'principal', 'Health Outcomes Z-score', 'Health Outcomes Rank', 'Health Factors Z-score', 'Health Factors Rank', ''], dtype='object')
In [141]:
abs_health['Health Factors Rank']=pd.to_numeric(abs_health.iloc[:,-2])
abs_health['Health Factors Z-score']=pd.to_numeric(abs_health.iloc[:,-3])
abs_health['Health Outcomes Z-score']=pd.to_numeric(abs_health.iloc[:,6])
abs_health['Health Outcomes Rank']=pd.to_numeric(abs_health.iloc[:,7])
In [142]:
corr = abs_health.corr()

# plot the heatmap
sns.heatmap(corr)
Out[142]:
<matplotlib.axes._subplots.AxesSubplot at 0x27368bea748>
In [160]:
abs_health2=pd.merge(tn_as1,health17subranks, left_on = 'NAME10', right_on='County')
In [161]:
abs_health2 = abs_health2.iloc[:,[0,1,2,3,4,5,9,10,11,12,13,14,15,16,17,18,19,20]]
In [162]:
abs_health2.head()
Out[162]:
NAME10 n_students n_chronically_absent pct classroom_teacher principal Length of Life Z-score Length of Life Rank Quality of Life Z-score Quality of Life Rank Health Behaviors Z-Score Health Behaviors Rank Clinical Care Z-score Clinical Care Rank Social & Economic Factors Z-score Social & Economic Factors Rank Physical Environment Z-score Physical Environment Rank
0 Anderson 11621 1432 0.123225 46386.0 81234.0 -0.20 30 -0.15 30 -0.11 19 -0.22 7 -0.10 31 0.01 57
1 Bedford 8502 1088 0.127970 45733.0 78245.0 -0.14 34 -0.01 46 0.03 56 0.06 67 -0.03 42 0.06 86
2 Benton 2123 192 0.090438 45908.0 64938.0 0.70 86 0.15 65 0.10 74 0.11 81 0.23 81 0.01 59
3 Bledsoe 1681 279 0.165973 48781.0 69205.0 -0.69 7 -0.15 32 0.33 93 0.19 90 0.41 93 -0.05 11
4 Blount 17779 1338 0.075257 61790.0 99120.0 -0.55 14 -0.50 9 -0.16 10 -0.18 9 -0.32 7 0.04 80
In [159]:
abs_health2a=abs_health2.iloc[:,1:].astype(float)
abs_health2a['County']=abs_health['NAME10']
abs_health2a.head()
Out[159]:
n_students n_chronically_absent pct classroom_teacher principal Length of Life Z-score Length of Life Rank Quality of Life Z-score Quality of Life Rank Health Behaviors Z-Score Health Behaviors Rank Clinical Care Z-score Clinical Care Rank Social & Economic Factors Z-score Social & Economic Factors Rank Physical Environment Z-score Physical Environment Rank County
0 11621.0 1432.0 0.123225 46386.0 81234.0 -0.20 30.0 -0.15 30.0 -0.11 19.0 -0.22 7.0 -0.10 31.0 0.01 57.0 Anderson
1 8502.0 1088.0 0.127970 45733.0 78245.0 -0.14 34.0 -0.01 46.0 0.03 56.0 0.06 67.0 -0.03 42.0 0.06 86.0 Bedford
2 2123.0 192.0 0.090438 45908.0 64938.0 0.70 86.0 0.15 65.0 0.10 74.0 0.11 81.0 0.23 81.0 0.01 59.0 Benton
3 1681.0 279.0 0.165973 48781.0 69205.0 -0.69 7.0 -0.15 32.0 0.33 93.0 0.19 90.0 0.41 93.0 -0.05 11.0 Bledsoe
4 17779.0 1338.0 0.075257 61790.0 99120.0 -0.55 14.0 -0.50 9.0 -0.16 10.0 -0.18 9.0 -0.32 7.0 0.04 80.0 Blount
In [163]:
corr = abs_health2a.corr()

# plot the heatmap
sns.heatmap(corr)
Out[163]:
<matplotlib.axes._subplots.AxesSubplot at 0x2736d53a550>
In [70]:
abs_health3=pd.merge(tn_as,disparaties17, left_on = 'NAME10', right_on='Unnamed: 2')
In [71]:
abs_health3.head()
Out[71]:
pct_chronically_absent STATEFP10 COUNTYFP10 COUNTYNS10 GEOID10 NAME10 NAMELSAD10 LSAD10 CLASSFP10 MTFCC10 CSAFP10 CBSAFP10 METDIVFP10 FUNCSTAT10 ALAND10 AWATER10 INTPTLAT10 INTPTLON10 geometry n_students n_chronically_absent pct classroom_teacher principal Unnamed: 0 Unnamed: 1 Unnamed: 2 Premature death Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9 Poor or fair health Unnamed: 11 Unnamed: 12 Unnamed: 13 Poor physical health days Unnamed: 15 Unnamed: 16 Unnamed: 17 Poor mental health days Unnamed: 19 Unnamed: 20 Unnamed: 21 Low birthweight Unnamed: 23 Unnamed: 24 Unnamed: 25 Unnamed: 26 Unnamed: 27 Unnamed: 28 Unnamed: 29 Adult smoking Unnamed: 31 Unnamed: 32 Unnamed: 33 Adult obesity Unnamed: 35 Unnamed: 36 Unnamed: 37 Food environment index Unnamed: 39 Physical inactivity Unnamed: 41 Unnamed: 42 Unnamed: 43 Access to exercise opportunities Unnamed: 45 Excessive drinking Unnamed: 47 Unnamed: 48 Unnamed: 49 Alcohol-impaired driving deaths Unnamed: 51 Unnamed: 52 Unnamed: 53 Unnamed: 54 Unnamed: 55 Sexually transmitted infections Unnamed: 57 Unnamed: 58 Teen births Unnamed: 60 Unnamed: 61 Unnamed: 62 Unnamed: 63 Unnamed: 64 Unnamed: 65 Uninsured Unnamed: 67 Unnamed: 68 Unnamed: 69 Unnamed: 70 Primary care physicians Unnamed: 72 Unnamed: 73 Unnamed: 74 Dentists Unnamed: 76 Unnamed: 77 Unnamed: 78 Mental health providers Unnamed: 80 Unnamed: 81 Unnamed: 82 Preventable hospital stays Unnamed: 84 Unnamed: 85 Unnamed: 86 Unnamed: 87 Mammography screening Unnamed: 89 Unnamed: 90 Unnamed: 91 Unnamed: 92 Flu vaccinations Unnamed: 94 Unnamed: 95 Unnamed: 96 Unnamed: 97 High school graduation Unnamed: 99 Unnamed: 100 Some college Unnamed: 102 Unnamed: 103 Unnamed: 104 Unnamed: 105 Unnamed: 106 Unemployment Unnamed: 108 Unnamed: 109 Unnamed: 110 Children in poverty Unnamed: 112 Unnamed: 113 Unnamed: 114 Unnamed: 115 Unnamed: 116 Unnamed: 117 Income inequality Unnamed: 119 Unnamed: 120 Unnamed: 121 Children in single-parent households Unnamed: 123 Unnamed: 124 Unnamed: 125 Unnamed: 126 Unnamed: 127 Social associations Unnamed: 129 Unnamed: 130 Violent crime Unnamed: 132 Unnamed: 133 Injury deaths Unnamed: 135 Unnamed: 136 Unnamed: 137 Unnamed: 138 Air pollution - particulate matter Unnamed: 140 Drinking water violations Unnamed: 142 Severe housing problems Unnamed: 144 Unnamed: 145 Unnamed: 146 Unnamed: 147 Unnamed: 148 Unnamed: 149 Driving alone to work Unnamed: 151 Unnamed: 152 Unnamed: 153 Unnamed: 154 Unnamed: 155 Unnamed: 156 Long commute - driving alone Unnamed: 158 Unnamed: 159 Unnamed: 160 Unnamed: 161
0 11.066667 47 001 01639722 47001 Anderson Anderson County 06 H1 G4020 314 28940 None A 873245292 19771988 +36.1167307 -084.1954177 POLYGON ((-84.33464599999999 36.0302, -84.3346... 11621 1432 0.123225 46386.0 81234.0 47001 Tennessee Anderson 9536 8703 10368 -0.40 11586 NaN 9749 19 18 20 -0.51 4.7 4.5 4.9 -0.47 4.7 4.5 4.9 -0.35 NaN 9 8 10 -0.10 12 NaN 9 21 20 22 -0.68 32 26 38 -0.56 7.5 0.23 28 23 34 -0.41 66 -0.62 14 13 14 0.00 18 60 30 23 37 0.14 263 347.2 -0.09 37 34 40 -0.19 29 24 38 5550 9 8 11 -1.31 46 61 1651:1 -0.49 55 72 1386:1 -2.03 69 90 1105:1 -0.26 4867 -0.58 5397 NaN 4825 49 -1.98 53 54 49 54 -1.49 42 43 55 859 92 0.15 10273 17614 58 54 63 -0.98 1333 34175 3.9 -0.51 22 16 28 -0.35 26 34 25 96851 19940 4.9 0.62 5894 15656 38 32 43 0.65 118 15.5 -1.22 251 401 0.05 404 107 96 117 0.46 10.3 0.55 No -0.59 13 12 15 12 1 1 -0.08 87 85 89 0.77 95 77 90 27323 34 31 37 -0.42
1 12.800000 47 003 01639723 47003 Bedford Bedford County 06 H1 G4020 None 43180 None A 1226710123 2983154 +35.5136604 -086.4582939 POLYGON ((-86.499674 35.359984, -86.499798 35.... 8502 1088 0.127970 45733.0 78245.0 47003 Tennessee Bedford 9762 8769 10754 -0.28 9265 4454 10683 22 21 22 0.45 4.8 4.6 5.0 -0.10 4.7 4.5 4.9 -0.20 NaN 9 8 10 -0.11 14 6 9 21 21 22 -0.36 33 26 39 -0.30 7.9 -0.38 33 26 39 0.79 51 0.13 15 14 15 0.68 24 47 51 45 57 2.08 153 324.3 -0.20 43 39 47 0.42 33 49 43 5173 13 11 15 1.19 18 38 2638:1 0.27 12 25 4010:1 0.57 33 69 1458:1 0.04 6034 0.09 3236 NaN 6300 42 -0.65 51 29 41 45 0.10 36 21 45 571 91 0.45 5013 12114 41 36 46 0.64 804 20539 3.9 -0.49 23 17 29 -0.19 29 29 19 82588 21083 3.9 -1.24 4316 11929 36 30 42 0.44 45 9.5 0.36 200 425 0.17 203 86 74 98 -0.50 10.5 0.95 Yes 1.66 15 13 18 12 4 1 0.72 82 79 85 -0.74 81 64 81 17179 36 31 40 -0.25
2 9.000000 47 005 01639724 47005 Benton Benton County 06 H1 G4020 None None None A 1020822586 109009079 +36.0692530 -088.0712118 POLYGON ((-87.95178799999999 36.225321, -87.95... 2123 192 0.090438 45908.0 64938.0 47005 Tennessee Benton 12828 10692 14965 1.40 NaN NaN NaN 21 20 22 0.32 5.2 5.0 5.5 1.07 5.1 4.9 5.4 1.34 NaN 8 7 10 -0.63 NaN NaN NaN 23 22 24 0.24 35 28 41 0.54 7.4 0.38 32 25 39 0.52 48 0.27 12 12 13 -1.23 11 27 41 31 50 1.13 56 347.2 -0.09 50 42 58 1.15 NaN NaN NaN 1431 12 10 13 0.41 3 19 5338:1 0.91 3 19 5329:1 0.91 6 38 2664:1 0.48 6528 0.37 NaN NaN NaN 32 1.25 NaN NaN NaN 47 -0.25 37 40 48 166 96 -0.77 1515 3422 44 36 52 0.36 364 6769 5.4 1.28 30 21 39 0.91 NaN 93 28 69035 14105 4.9 0.69 1152 3209 36 25 47 0.40 21 13.1 -0.59 25 152 -1.15 130 161 134 189 3.00 10.0 -0.06 No -0.59 16 13 19 13 3 1 1.07 87 83 91 0.83 NaN NaN NaN 4807 27 22 32 -1.04
3 16.600000 47 007 01639725 47007 Bledsoe Bledsoe County 06 H1 G4020 None None None A 1052635810 838365 +35.5936682 -085.2059790 POLYGON ((-85.277034 35.390432, -85.277891 35.... 1681 279 0.165973 48781.0 69205.0 47007 Tennessee Bledsoe 7735 6103 9367 -1.39 NaN NaN NaN 22 21 23 0.64 5.1 4.9 5.4 0.81 4.8 4.5 5.0 0.05 NaN 7 5 9 -1.51 NaN NaN NaN 26 25 27 1.64 35 28 42 0.63 7.8 -0.23 29 23 36 -0.30 25 1.41 14 13 15 0.13 4 8 50 32 65 1.98 180 1241.2 3.00 42 34 51 0.31 NaN NaN NaN 1331 14 12 16 1.88 1 7 14675:1 1.31 3 20 4906:1 0.82 4 27 3679:1 0.62 5025 -0.49 NaN NaN NaN 36 0.49 NaN NaN NaN 34 2.04 NaN NaN NaN 124 85 1.79 1329 3954 34 27 40 1.38 253 4317 5.9 1.87 30 21 40 0.97 20 84 25 81994 17665 4.6 0.19 679 2313 29 21 38 -0.55 7 4.8 1.58 24 173 -1.05 60 85 65 109 -0.56 9.4 -1.26 No -0.59 18 13 22 13 3 2 1.73 77 71 83 -2.32 NaN NaN NaN 3942 51 40 61 1.17
4 7.100000 47 009 01639726 47009 Blount Blount County 06 H1 G4020 314 28940 None A 1447041818 20307831 +35.6881849 -083.9229731 POLYGON ((-84.14251999999999 35.796954, -84.14... 17779 1338 0.075257 61790.0 99120.0 47009 Tennessee Blount 8268 7676 8860 -1.09 8794 NaN 8458 17 16 17 -1.44 4.6 4.4 4.8 -0.78 4.5 4.3 4.7 -0.96 NaN 8 7 8 -0.92 11 4 8 19 18 20 -1.47 35 29 41 0.54 7.7 -0.08 26 21 31 -1.15 67 -0.66 14 13 15 0.32 34 109 31 26 36 0.25 473 371.7 0.04 30 28 32 -0.83 32 39 30 10388 10 9 11 -0.69 92 72 1399:1 -0.85 75 58 1732:1 -1.24 154 119 844:1 -0.65 4892 -0.57 5676 8481 4884 45 -1.22 61 30 45 54 -1.49 43 37 54 1330 94 -0.44 17521 29975 58 55 62 -0.99 2152 61819 3.5 -1.02 17 13 21 -1.13 28 39 17 96133 22746 4.2 -0.63 6977 26229 27 23 30 -0.95 146 11.3 -0.13 449 354 -0.18 517 81 74 88 -0.74 10.1 0.15 Yes 1.66 13 11 14 12 1 1 -0.40 86 84 87 0.41 90 74 87 49316 36 33 38 -0.23
In [72]:
abs_health_3=abs_health3.iloc[:,[5,21,22,23,38,42,47,54,58,62,64,83,91,95,99,103,117,133,135,148,155,167]]
In [73]:
abs_health_3.columns = ('NAME10', 'pct', 'classroom_teacher', 'principal', 'Poor physical health days', 'Poor mental health days', '% Low Birth Weight', 'Adult smoking', 'Adult obesity', 'Food environment index', 'Physical inactivity', 'Teen births', '% Uninsured', 'Primary care physicians', 'Dentists', 'Mental health providers', 'Flu vaccinations', '% Unemployed', 'Children in poverty', '% Single-Parent Household', 'Violent crime', 'Severe housing problems')
In [166]:
abs_health_3a = abs_health_3.iloc[:,1:].astype(float)
abs_health_3a['County'] = abs_health3['NAME10']
In [167]:
corr = abs_health_3a.corr()

# plot the heatmap
sns.heatmap(corr)
Out[167]:
<matplotlib.axes._subplots.AxesSubplot at 0x2736d638630>
In [91]:
demographics = more_disparaties17.iloc[:,89:]
In [92]:
demographics['County'] = more_disparaties17['Unnamed: 2']

C:\Users\unews\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.
In [93]:
demographics.columns=demographics.iloc[0,:]
In [94]:
demographics = demographics.iloc[1,:]
In [ ]:
abs_health4=pd.merge(tn_as,more_disparaties17, left_on = 'NAME10', right_on='Unnamed: 2')
In [96]:
more=more_disparaties17.iloc[:,[2,3,9,16,17,23,24,30,33,36,39,40,41,42,43,44,45,46,51,54,55,58,59,65,71,74,77,81,82,85,86]]
In [97]:
more.columns = ('County', 'Life expectancy', 'Premature age-adjusted mortality', 'Child mortality # Deaths', 'Child Mortality Rate', 'Infant mortality # of Deaths', 'Infant Mortality Rate', '% Frequent physical distress', '%Frequent mental distress', '% Diabetes prevalence', 'HIV prevalence # of cases', 'HIV Prevalence Rate', '# of Food insecure', '% of Food Insecure', '# with Limited access to healthy foods', '% of limited access to healthy foods', '# of Drug overdose deaths', 'Drug Overdose Mortality Rate', '% Insufficient sleep', '# of Uninsured adults', '% of Uninsured Adults', '# of Uninsured children', '% of Uninsured children', 'Median household income', '%Children eligible for free or reduced price lunch', 'Homicides', '# of Firearm fatalities', '# of Homeowners', '% of Homeowners', '# of Households with Severe housing cost burden', '% of Severe Housing Cost Burden')
In [98]:
more_disp=pd.merge(tn_as1,more, left_on = 'NAME10', right_on = 'County')##USE
In [116]:
type(more_disp.iloc[1,18])
Out[116]:
str
In [183]:
more_disp.columns
Out[183]:
Index(['NAME10', 'n_students', 'n_chronically_absent', 'pct', 'classroom_teacher', 'principal', 'Life expectancy', 'Premature age-adjusted mortality', 'Child mortality # Deaths', 'Child Mortality Rate', 'Infant mortality # of Deaths', 'Infant Mortality Rate', '% Frequent physical distress', '%Frequent mental distress', '% Diabetes prevalence', 'HIV prevalence # of cases', 'HIV Prevalence Rate', '# of Food insecure', '% of Food Insecure', '# with Limited access to healthy foods', '% of limited access to healthy foods', '# of Drug overdose deaths', 'Drug Overdose Mortality Rate', '% Insufficient sleep', '# of Uninsured adults', '% of Uninsured Adults', '# of Uninsured children', '% of Uninsured children', 'Median household income', '%Children eligible for free or reduced price lunch', 'Homicides', '# of Firearm fatalities', '# of Homeowners', '% of Homeowners', '# of Households with Severe housing cost burden', '% of Severe Housing Cost Burden'], dtype='object')
In [102]:
more_disparaties17
Out[102]:
Unnamed: 0 Unnamed: 1 Unnamed: 2 Life expectancy Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Premature age-adjusted mortality Unnamed: 10 Unnamed: 11 Unnamed: 12 Unnamed: 13 Unnamed: 14 Unnamed: 15 Child mortality Unnamed: 17 Unnamed: 18 Unnamed: 19 Unnamed: 20 Unnamed: 21 Unnamed: 22 Infant mortality Unnamed: 24 Unnamed: 25 Unnamed: 26 Unnamed: 27 Unnamed: 28 Unnamed: 29 Frequent physical distress Unnamed: 31 Unnamed: 32 Frequent mental distress Unnamed: 34 Unnamed: 35 Diabetes prevalence Unnamed: 37 Unnamed: 38 HIV prevalence Unnamed: 40 Food insecurity Unnamed: 42 Limited access to healthy foods Unnamed: 44 Drug overdose deaths Unnamed: 46 Motor vehicle crash deaths Unnamed: 48 Unnamed: 49 Unnamed: 50 Insufficient sleep Unnamed: 52 Unnamed: 53 Uninsured adults Unnamed: 55 Unnamed: 56 Unnamed: 57 Uninsured children Unnamed: 59 Unnamed: 60 Unnamed: 61 Other primary care providers Unnamed: 63 Disconnected youth Median household income Unnamed: 66 Unnamed: 67 Unnamed: 68 Unnamed: 69 Unnamed: 70 Children eligible for free or reduced price lunch Residential segregation - black/white Residential segregation - non-white/white Homicides Unnamed: 75 Unnamed: 76 Firearm fatalities Unnamed: 78 Unnamed: 79 Unnamed: 80 Homeownership Unnamed: 82 Unnamed: 83 Unnamed: 84 Severe housing cost burden Unnamed: 86 Unnamed: 87 Unnamed: 88 Demographics Unnamed: 90 Unnamed: 91 Unnamed: 92 Unnamed: 93 Unnamed: 94 Unnamed: 95 Unnamed: 96 Unnamed: 97 Unnamed: 98 Unnamed: 99 Unnamed: 100 Unnamed: 101 Unnamed: 102 Unnamed: 103 Unnamed: 104 Unnamed: 105 Unnamed: 106 Unnamed: 107 Unnamed: 108 Unnamed: 109 Unnamed: 110
0 FIPS State County Life Expectancy 95% CI - Low 95% CI - High Life Expectancy (Black) Life Expectancy (Hispanic) Life Expectancy (White) # Deaths Age-Adjusted Mortality 95% CI - Low 95% CI - High Age-Adjusted Mortality (Black) Age-Adjusted Mortality (Hispanic) Age-Adjusted Mortality (White) # Deaths Child Mortality Rate 95% CI - Low 95% CI - High Child Mortality Rate (Black) Child Mortality Rate (Hispanic) Child Mortality Rate (White) # Deaths Infant Mortality Rate 95% CI - Low 95% CI - High Infant Mortality Rate (Black) Infant Mortality Rate (Hispanic) Infant Mortality Rate (White) % Frequent Physical Distress 95% CI - Low 95% CI - High % Frequent Mental Distress 95% CI - Low 95% CI - High % Diabetic 95% CI - Low 95% CI - High # HIV Cases HIV Prevalence Rate # Food Insecure % Food Insecure # Limited Access % Limited Access # Drug Overdose Deaths Drug Overdose Mortality Rate # Motor Vehicle Deaths MV Mortality Rate 95% CI - Low 95% CI - High % Insufficient Sleep 95% CI - Low 95% CI - High # Uninsured % Uninsured 95% CI - Low 95% CI - High # Uninsured % Uninsured 95% CI - Low 95% CI - High Other PCP Rate Other PCP Ratio % Disconnected Youth Household Income 95% CI - Low 95% CI - High Household income (Black) Household income (Hispanic) Household income (White) % Free or Reduced Lunch Segregation index Segregation Index Homicide Rate 95% CI - Low 95% CI - High # Firearm Fatalities Firearm Fatalities Rate 95% CI - Low 95% CI - High # Homeowners % Homeowners 95% CI - Low 95% CI - High # Households with Severe Cost Burden % Severe Housing Cost Burden 95% CI - Low 95% CI - High Population % < 18 % 65 and over # African American % African American # American Indian/Alaskan Native % American Indian/Alaskan Native # Asian % Asian # Native Hawaiian/Other Pacific Islander % Native Hawaiian/Other Pacific Islander # Hispanic % Hispanic # Non-Hispanic White % Non-Hispanic White # Not Proficient in English % Not Proficient in English 95% CI - Low 95% CI - High % Female # Rural % Rural
1 47000 Tennessee NaN 76.1 76.0 76.2 NaN NaN NaN 104195 447 444 449 NaN NaN NaN 3725 62 60 64 NaN NaN NaN 4045 7 7 7 NaN NaN NaN 14 13 15 14 13 15 13 NaN NaN 16425 297 967430 15 536900 8 4863 24 6936 15 15 16 36 35 38 530070 13 13 14 56887 4 3 4 127 787:1 8 51319 50836 51802 NaN NaN NaN NaN 67 58 7 7 7 5513 17 16 17 1688565 66 66 67 311113 13 13 13 6715984 22.4 16.0 1126692 16.8 30876 0.5 125244 1.9 6428 0.1 366554 5.5 4963780 73.9 94475 2 1 2 51.2 2132860 33.6
2 47001 Tennessee Anderson 76.1 75.4 76.8 74.8 92.3 75.9 1299 451 426 477 536 NaN 460 43 67 49 91 NaN NaN NaN 45 8 6 11 NaN NaN NaN 14 13 14 14 13 14 12 9 15 91 141 10160 13 7031 9 104 46 81 15 12 19 36 35 37 5099 11 10 13 497 3 2 4 111 897:1 6 48679 43944 53414 38024 39228 48161 NaN 51 38 3 2 5 51 14 10 18 20584 67 66 69 3461 12 10 14 76257 21.2 19.8 3022 4.0 344 0.5 1201 1.6 41 0.1 2188 2.9 68059 89.2 734 1 1 1 51.3 26041 34.7
3 47003 Tennessee Bedford 74.8 74.0 75.6 74.7 85.8 74.1 799 489 454 524 495 281 512 34 69 48 97 NaN NaN NaN 36 8 6 11 NaN NaN NaN 14 14 15 14 14 14 12 9 16 46 121 5690 12 2984 7 19 13 67 21 16 26 36 35 37 4627 17 14 19 591 5 3 6 98 1024:1 12 50904 45339 56469 35304 39554 48796 NaN 41 33 NaN NaN NaN 32 14 9 19 11631 68 66 71 2292 14 11 16 48117 25.5 15.1 3598 7.5 525 1.1 520 1.1 83 0.2 5822 12.1 37170 77.2 1463 3 2 4 50.9 25053 55.6
4 47005 Tennessee Benton 72.6 71.2 74.1 NaN NaN NaN 409 606 542 671 NaN NaN NaN 10 79 38 145 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 15 16 15 15 16 14 11 19 13 94 2450 15 1082 7 17 35 38 34 24 46 37 36 38 1308 14 12 17 133 4 3 6 100 999:1 NaN 36034 31656 40412 20625 36635 34255 NaN 45 35 NaN NaN NaN 20 25 15 38 5066 76 72 79 794 13 10 16 15986 19.5 23.7 368 2.3 87 0.5 97 0.6 2 0.0 375 2.3 14851 92.9 36 0 0 1 50.9 12937 78.5
5 47007 Tennessee Bledsoe 78.1 76.5 79.6 NaN NaN NaN 233 396 344 449 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 15 16 15 14 15 14 10 18 57 440 2200 16 211 2 NaN NaN 13 13 7 23 37 36 38 1220 17 14 20 120 5 3 7 68 1472:1 NaN 38261 33484 43038 NaN NaN NaN NaN 56 23 NaN NaN NaN 15 21 12 35 3533 76 71 81 373 9 5 12 14717 16.0 18.1 1026 7.0 81 0.6 35 0.2 5 0.0 339 2.3 13058 88.7 260 2 0 4 41.2 12876 100.0
6 47009 Tennessee Blount 77.6 77.1 78.1 80.1 90.8 77.3 1925 391 372 409 465 NaN 397 57 54 41 70 NaN NaN NaN 49 5 4 7 NaN NaN NaN 13 13 14 13 13 13 15 11 18 118 109 14160 11 14303 12 121 31 106 12 10 14 33 32 35 9518 12 11 14 952 3 2 5 82 1214:1 6 51391 48123 54659 28322 31406 52333 NaN 63 36 2 1 3 88 14 11 17 37372 75 74 76 5387 11 10 13 129929 20.4 19.7 3800 2.9 545 0.4 1171 0.9 75 0.1 4285 3.3 118326 91.1 1042 1 1 1 51.5 40140 32.6
7 47011 Tennessee Bradley 76.9 76.3 77.4 76.4 89.5 76.5 1591 424 402 445 460 NaN 438 48 52 38 69 NaN NaN NaN 55 7 5 9 NaN NaN NaN 15 14 15 14 13 14 15 12 18 104 119 13560 13 11906 12 66 21 100 14 11 17 37 36 38 10118 16 14 18 1017 4 3 6 108 926:1 4 48663 44067 53259 34583 29098 48829 NaN 42 29 4 3 6 64 12 10 16 26120 66 64 68 4494 12 10 13 105560 22.0 16.8 5033 4.8 642 0.6 1240 1.2 120 0.1 6608 6.3 90607 85.8 1633 2 1 2 51.4 32630 33.0
8 47013 Tennessee Campbell 72.0 71.1 72.9 NaN NaN NaN 1018 660 617 702 NaN NaN NaN 20 60 37 93 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 15 16 15 15 16 14 11 18 20 59 6200 16 4113 10 44 37 69 25 19 31 34 33 35 3374 15 12 17 262 3 2 4 136 734:1 8 40866 37365 44367 51641 56250 35344 NaN 68 45 4 2 7 24 12 8 18 10980 69 66 72 1528 10 9 12 39648 20.7 20.4 183 0.5 127 0.3 133 0.3 30 0.1 513 1.3 38233 96.4 26 0 0 0 51.0 22403 55.0
9 47015 Tennessee Cannon 74.3 72.8 75.7 NaN NaN NaN 287 552 484 619 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 13 14 14 13 14 13 9 18 19 161 1700 12 146 1 15 36 28 29 19 42 34 33 36 1223 15 12 17 126 4 3 6 35 2843:1 NaN 51795 46635 56955 NaN 31875 47903 NaN 26 3 10 5 19 15 22 12 36 4015 74 70 78 394 8 5 11 14216 21.3 18.1 218 1.5 50 0.4 49 0.3 15 0.1 332 2.3 13352 93.9 25 0 0 1 50.3 11197 81.1
10 47017 Tennessee Carroll 72.8 71.7 73.8 74.8 NaN 72.5 635 589 540 637 522 NaN 603 15 62 35 103 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 14 15 14 14 15 14 11 18 30 126 4410 16 964 3 10 12 49 25 18 33 36 35 37 1900 12 10 14 220 4 2 5 154 648:1 17 41492 37019 45965 28801 23345 39274 NaN 22 21 NaN NaN NaN 25 18 12 26 8204 72 70 75 1422 13 10 16 27860 21.3 20.2 2815 10.1 166 0.6 102 0.4 13 0.0 735 2.6 23573 84.6 26 0 0 0 51.2 23690 83.1
11 47019 Tennessee Carter 76.1 75.3 76.9 NaN NaN NaN 1035 459 429 489 NaN NaN NaN 27 63 41 91 NaN NaN NaN 24 7 4 10 NaN NaN NaN 16 15 16 15 15 16 15 12 19 63 129 8600 15 11750 20 52 31 45 11 8 15 36 35 37 4687 14 12 16 352 3 2 4 44 2260:1 9 39725 36626 42824 NaN 27976 34911 NaN 69 36 NaN NaN NaN 42 15 11 20 16656 70 68 72 3024 13 12 15 56488 18.8 21.4 830 1.5 158 0.3 230 0.4 15 0.0 1102 2.0 53510 94.7 98 0 0 0 51.0 23524 41.0
12 47021 Tennessee Cheatham 74.0 73.2 74.9 NaN NaN NaN 729 503 466 541 NaN NaN NaN 20 54 33 84 NaN NaN NaN 22 7 4 11 NaN NaN NaN 13 12 13 13 12 13 13 10 17 58 174 4040 10 222 1 56 47 60 22 17 28 34 32 35 2889 12 10 13 336 4 2 5 77 1301:1 12 59103 54163 64043 52841 39005 57018 NaN 40 22 6 3 9 38 19 14 26 11599 79 77 81 1539 11 9 13 40330 22.7 14.4 690 1.7 212 0.5 186 0.5 28 0.1 1198 3.0 37451 92.9 33 0 0 0 50.4 32442 83.0
13 47023 Tennessee Chester 77.5 76.2 78.7 79.5 NaN 77.2 250 403 351 455 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 13 14 14 13 14 13 9 17 8 54 2400 14 980 6 10 19 22 18 11 28 35 34 36 1403 15 12 17 149 4 2 5 82 1223:1 NaN 45261 39707 50815 24045 41447 49571 NaN 49 38 NaN NaN NaN 10 12 6 21 4500 75 71 79 636 11 7 15 17119 22.2 17.1 1543 9.0 87 0.5 99 0.6 5 0.0 446 2.6 14659 85.6 50 0 0 1 52.0 11177 65.2
14 47025 Tennessee Claiborne 73.5 72.5 74.4 NaN NaN NaN 690 555 511 598 NaN NaN NaN 12 49 25 85 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 14 15 15 14 15 14 10 18 13 47 4780 15 3291 10 38 40 54 24 18 32 35 34 36 2268 12 10 14 211 3 2 4 158 632:1 NaN 37886 35037 40735 22895 26597 35941 NaN 69 36 NaN NaN NaN 38 24 17 33 9234 71 68 74 1782 15 12 18 31609 19.1 19.5 379 1.2 110 0.3 225 0.7 7 0.0 415 1.3 30094 95.2 105 0 0 1 51.1 23050 71.6
15 47027 Tennessee Clay 72.1 69.6 74.5 NaN NaN NaN 199 645 546 745 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 18 17 18 17 16 17 14 10 18 7 105 1200 15 5 0 11 47 10 18 9 34 36 35 37 653 15 13 18 66 4 3 6 65 1541:1 NaN 34035 29524 38546 NaN NaN NaN NaN 35 24 NaN NaN NaN 17 44 26 70 2359 74 66 82 290 10 5 15 7703 20.1 24.1 123 1.6 27 0.4 7 0.1 8 0.1 179 2.3 7280 94.5 50 1 0 2 50.6 7861 100.0
16 47029 Tennessee Cocke 71.9 70.9 72.9 NaN NaN NaN 892 635 590 680 NaN NaN NaN 20 69 42 106 NaN NaN NaN 20 7 5 12 NaN NaN NaN 16 16 17 16 15 16 18 13 22 26 87 5790 16 4170 12 25 24 58 23 18 30 37 36 38 2792 14 11 16 231 3 2 4 84 1185:1 19 35788 31642 39934 24079 26071 32349 NaN 59 46 6 3 10 33 19 13 26 9964 68 65 71 1720 13 10 16 35556 20.4 20.7 718 2.0 212 0.6 156 0.4 25 0.1 857 2.4 33116 93.1 37 0 0 0 51.6 24083 67.5
17 47031 Tennessee Coffee 74.3 73.5 75.0 72.2 86.1 74.1 1033 529 496 563 610 NaN 538 26 50 33 73 NaN NaN NaN 22 5 3 7 NaN NaN NaN 14 13 14 14 13 14 13 10 16 46 102 6660 12 3899 7 46 28 76 20 16 25 36 34 37 4372 14 12 16 519 4 3 5 147 679:1 7 48188 43103 53273 34375 27447 47033 NaN 60 24 4 2 7 40 15 11 20 14625 68 65 70 2324 11 9 13 55034 24.0 17.2 1977 3.6 254 0.5 588 1.1 33 0.1 2328 4.2 48893 88.8 408 1 0 1 51.2 24967 47.3
18 47033 Tennessee Crockett 75.5 74.2 76.8 76.4 NaN 74.7 244 447 389 505 470 NaN 471 10 71 34 131 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 16 16 15 15 15 13 9 17 14 116 1980 14 38 0 NaN NaN 24 24 15 35 36 35 37 1449 17 15 20 184 5 3 7 76 1316:1 NaN 40480 35665 45295 30089 40473 42676 NaN 13 11 NaN NaN NaN 13 18 10 31 3770 70 66 73 558 11 8 14 14473 24.0 18.5 1983 13.7 90 0.6 46 0.3 4 0.0 1523 10.5 10698 73.9 228 2 1 2 52.3 9828 67.4
19 47035 Tennessee Cumberland 76.9 76.1 77.7 NaN 86.5 76.7 1098 439 409 470 NaN NaN NaN 19 45 27 70 NaN NaN NaN 27 7 5 10 NaN NaN NaN 14 13 14 13 13 14 17 13 20 39 77 7040 12 4498 8 28 16 89 22 18 27 37 35 38 4607 15 13 17 442 4 3 5 93 1074:1 15 45118 41190 49046 NaN 38021 41051 NaN 80 40 4 2 6 63 22 17 28 19627 78 77 80 2599 11 9 12 59078 17.9 30.2 351 0.6 268 0.5 360 0.6 64 0.1 1766 3.0 55732 94.3 439 1 0 1 51.3 34132 60.9
20 47037 Tennessee Davidson 77.0 76.8 77.2 74.4 85.7 77.6 8532 408 399 417 508 181 394 444 76 69 84 106 57 60 507 7 7 8 12 5 5 13 13 13 13 13 13 11 9 12 3444 607 103900 16 46009 7 618 30 479 10 9 11 39 38 40 66519 15 14 16 7257 5 4 6 203 493:1 6 58264 55596 60932 39046 41521 63080 NaN 48 41 10 9 11 556 16 15 18 148731 54 54 55 38762 15 14 15 691243 21.1 11.9 188619 27.3 3453 0.5 26343 3.8 784 0.1 71071 10.3 388640 56.2 30547 5 5 5 51.8 21382 3.4
21 47039 Tennessee Decatur 75.4 73.8 77.0 NaN NaN NaN 231 484 416 552 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 14 15 14 14 15 15 11 19 12 121 1690 15 346 3 10 28 16 20 11 32 33 32 35 962 15 13 17 130 5 3 7 136 734:1 22 38988 34049 43927 19375 19423 38472 NaN 43 26 NaN NaN NaN NaN NaN NaN NaN 3473 74 70 77 566 13 8 17 11751 21.1 23.0 362 3.1 34 0.3 67 0.6 12 0.1 390 3.3 10769 91.6 0 0 0 1 50.8 11757 100.0
22 47041 Tennessee Dekalb 73.5 72.2 74.8 NaN NaN NaN 386 536 480 593 NaN NaN NaN 14 82 45 137 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 14 15 14 14 15 14 10 18 34 210 2690 14 142 1 14 24 23 17 11 26 35 34 36 1826 16 14 18 195 5 3 6 60 1654:1 NaN 42080 37904 46256 47448 27090 41186 NaN 30 38 NaN NaN NaN 14 15 8 24 4896 67 63 70 663 10 7 12 19852 21.8 18.0 270 1.4 84 0.4 231 1.2 3 0.0 1594 8.0 17479 88.0 82 0 0 1 50.4 14673 78.4
23 47043 Tennessee Dickson 74.2 73.4 75.0 69.6 84.7 74.2 947 515 481 549 780 NaN 509 30 62 42 88 NaN NaN NaN 22 5 3 8 NaN NaN NaN 13 13 14 14 13 14 13 9 17 62 145 6210 12 2577 5 48 31 96 27 22 33 36 34 37 4459 14 12 16 463 4 2 5 112 896:1 8 49518 44513 54523 29472 36079 48578 NaN 31 27 6 4 9 56 22 16 28 13571 71 69 73 1922 11 9 13 52853 23.4 15.7 2153 4.1 257 0.5 355 0.7 30 0.1 1892 3.6 47330 89.6 258 1 0 1 50.9 33650 67.8
24 47045 Tennessee Dyer 73.8 72.9 74.7 71.9 NaN 73.9 769 559 518 600 697 NaN 552 25 69 45 102 NaN NaN NaN 34 10 7 14 NaN NaN NaN 15 14 15 14 14 14 16 13 20 30 96 6050 16 5235 14 10 9 51 19 14 25 36 35 37 2735 12 10 15 320 3 2 5 123 814:1 13 44549 39694 49404 32348 48885 45462 NaN 45 42 NaN NaN NaN 25 13 9 20 9428 62 58 65 1492 10 8 12 37463 24.0 17.3 5221 13.9 150 0.4 295 0.8 12 0.0 1345 3.6 29947 79.9 194 1 0 1 51.8 16432 42.9
25 47047 Tennessee Fayette 77.7 76.8 78.6 74.5 NaN 78.6 624 385 352 418 506 NaN 349 16 50 29 81 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 13 12 13 13 12 13 15 11 18 89 265 5690 15 932 2 15 13 44 16 12 22 35 34 36 2608 11 9 13 320 4 3 5 45 2224:1 9 60112 54846 65378 32285 22415 68108 NaN 33 31 7 4 10 33 17 12 24 12028 80 78 81 1533 11 9 13 40036 19.6 20.7 10963 27.4 138 0.3 279 0.7 16 0.0 1077 2.7 27255 68.1 149 0 0 1 50.7 30363 79.0
26 47049 Tennessee Fentress 73.5 72.3 74.8 NaN NaN NaN 429 568 510 626 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 17 16 17 16 16 17 15 11 19 9 59 2660 15 19 0 10 18 31 25 17 35 33 32 34 1572 15 13 18 169 4 3 6 77 1295:1 NaN 32895 29057 36733 NaN 25197 33505 NaN NaN 24 NaN NaN NaN NaN NaN NaN NaN 5580 76 72 79 817 12 8 16 18136 21.2 21.4 69 0.4 61 0.3 56 0.3 4 0.0 285 1.6 17489 96.4 9 0 0 1 51.1 17959 100.0
27 47051 Tennessee Franklin 75.6 74.7 76.5 74.1 NaN 75.4 710 453 417 488 480 NaN 460 25 73 47 108 NaN NaN NaN 21 8 5 12 NaN NaN NaN 14 13 14 14 13 14 14 11 17 21 59 5150 13 2906 7 28 22 60 21 16 27 35 34 36 3120 13 11 15 345 4 3 5 65 1543:1 NaN 49596 44363 54829 38500 35122 47948 NaN 38 30 NaN NaN NaN 27 13 9 19 12015 74 71 76 1551 10 8 12 41652 20.2 19.7 2106 5.1 181 0.4 385 0.9 34 0.1 1446 3.5 36834 88.4 435 1 0 2 51.4 28579 69.6
28 47053 Tennessee Gibson 73.7 72.9 74.5 70.1 NaN 74.5 971 552 516 588 711 NaN 521 31 64 44 92 125 NaN 51 32 8 5 11 NaN NaN NaN 15 14 15 15 14 15 15 12 19 74 181 7840 16 3196 6 30 20 71 20 16 26 35 34 36 3294 12 10 13 411 3 2 4 88 1142:1 NaN 42859 38393 47325 25081 NaN 46924 NaN 43 40 7 4 10 45 18 13 24 13438 70 67 72 2086 11 9 14 49111 24.6 17.7 8981 18.3 154 0.3 143 0.3 12 0.0 1354 2.8 37791 77.0 101 0 0 1 52.0 23706 47.7
29 47055 Tennessee Giles 74.9 73.8 76.0 74.2 NaN 74.8 535 481 438 525 468 NaN 490 19 78 47 121 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 14 15 14 13 14 15 11 18 39 157 3920 14 1492 5 23 26 62 30 23 39 35 34 36 2411 14 12 16 273 4 3 6 92 1089:1 6 47838 42472 53204 40238 NaN 44772 NaN 21 20 NaN NaN NaN 31 21 15 30 8086 70 67 72 937 9 6 11 29401 21.0 19.7 3088 10.5 116 0.4 140 0.5 13 0.0 687 2.3 24813 84.4 137 1 0 1 51.4 21744 73.7
30 47057 Tennessee Grainger 74.2 73.0 75.4 NaN NaN NaN 476 518 468 568 NaN NaN NaN 18 94 56 149 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 14 15 14 14 15 14 10 19 17 87 2970 13 0 0 10 14 44 27 20 37 36 35 37 2033 15 12 17 217 4 3 6 43 2314:1 NaN 43190 38971 47409 10188 53100 40691 NaN NaN 26 NaN NaN NaN NaN NaN NaN NaN 7061 77 76 79 768 9 6 11 23144 20.6 20.0 164 0.7 92 0.4 56 0.2 14 0.1 748 3.2 21869 94.5 202 1 0 2 49.6 22657 100.0
31 47059 Tennessee Greene 74.5 73.9 75.2 NaN 94.2 74.2 1412 518 489 547 438 NaN 529 32 59 40 83 NaN NaN NaN 33 7 5 10 NaN NaN NaN 14 14 15 14 14 15 15 12 18 51 86 9230 14 2503 4 43 21 92 19 15 24 38 37 39 5324 13 11 15 482 3 2 5 112 894:1 9 40145 35634 44656 26271 21282 38777 NaN 59 41 4 3 7 50 15 11 19 19864 73 71 74 2660 10 9 12 68808 19.7 21.3 1415 2.1 237 0.3 411 0.6 56 0.1 1954 2.8 64015 93.0 216 0 0 1 50.8 44874 65.2
32 47061 Tennessee Grundy 71.6 70.0 73.1 NaN NaN NaN 332 635 562 708 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 17 16 17 16 16 17 16 12 21 8 70 2220 17 98 1 NaN NaN 39 41 29 57 36 35 37 1321 18 15 20 121 4 3 6 82 1215:1 NaN 34454 30101 38807 NaN NaN NaN NaN NaN 20 NaN NaN NaN 14 21 11 35 3670 75 72 78 488 11 8 15 13361 21.9 20.8 77 0.6 88 0.7 50 0.4 1 0.0 177 1.3 12816 95.9 15 0 0 1 50.5 13703 100.0
33 47063 Tennessee Hamblen 75.0 74.3 75.8 71.7 108.3 74.6 1180 507 477 537 689 172 525 30 51 34 72 NaN NaN NaN 33 6 4 9 NaN NaN NaN 15 15 16 15 14 15 15 11 19 51 97 8420 13 11827 19 54 28 64 14 11 18 37 36 38 6194 17 14 19 621 4 3 5 157 636:1 6 44181 39930 48432 26779 21696 45593 NaN 51 38 3 2 5 47 15 11 20 16081 66 64 68 3084 13 11 16 64277 23.1 18.3 2417 3.8 546 0.8 620 1.0 162 0.3 7670 11.9 52283 81.3 1520 3 2 3 51.1 13680 21.9
34 47065 Tennessee Hamilton 77.6 77.3 77.9 73.8 93.0 78.1 5112 401 390 412 561 123 382 183 61 52 70 104 37 50 214 7 6 8 13 NaN 6 13 12 13 13 13 13 13 12 15 888 297 50180 14 46488 14 215 20 264 11 9 12 37 36 37 26737 12 11 14 2204 3 2 4 167 599:1 6 52096 48522 55670 30035 38659 57241 NaN 61 54 9 7 10 287 16 14 18 89631 64 64 65 16980 13 12 13 361613 20.9 17.2 69540 19.2 1976 0.5 7632 2.1 531 0.1 20621 5.7 257150 71.1 4986 1 1 2 51.8 33721 10.0
35 47067 Tennessee Hancock 70.6 68.4 72.8 NaN NaN NaN 167 664 556 773 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 17 16 17 16 16 17 14 10 19 NaN NaN 1110 17 26 0 NaN NaN 14 30 16 51 37 36 38 560 15 12 17 46 3 2 5 167 600:1 NaN 29689 25790 33588 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2094 77 72 82 190 8 4 12 6600 21.0 20.7 33 0.5 23 0.3 13 0.2 3 0.0 34 0.5 6400 97.0 29 0 0 2 50.8 6819 100.0
36 47069 Tennessee Hardeman 74.3 73.2 75.4 74.0 NaN 74.1 501 528 480 576 551 NaN 532 15 74 41 122 NaN NaN NaN 20 11 7 17 NaN NaN NaN 16 15 16 15 14 15 15 12 19 39 176 5290 20 5218 19 10 13 40 22 16 30 41 40 42 1728 14 12 16 172 3 2 5 67 1497:1 19 38426 34537 42315 30413 127813 40195 NaN 44 42 12 8 18 22 17 11 26 6039 70 66 73 1102 13 10 16 25447 19.7 17.7 10600 41.7 81 0.3 184 0.7 3 0.0 454 1.8 13855 54.4 55 0 0 1 45.2 21859 80.2
37 47071 Tennessee Hardin 73.0 71.9 74.2 NaN NaN NaN 622 604 553 655 NaN NaN NaN 15 70 39 116 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 14 15 14 14 15 14 10 17 29 132 3750 15 1854 7 24 31 51 28 21 37 33 32 34 2102 14 12 17 208 4 3 5 120 834:1 NaN 39318 34768 43868 21146 21771 40201 NaN 41 37 6 3 11 29 22 15 32 7596 75 72 78 923 10 7 13 25846 20.6 22.6 852 3.3 158 0.6 144 0.6 8 0.0 683 2.6 23688 91.7 143 1 0 1 51.3 17679 67.9
38 47073 Tennessee Hawkins 74.7 73.9 75.5 NaN NaN NaN 1129 506 474 537 NaN NaN NaN 29 62 42 89 NaN NaN NaN 42 11 8 15 NaN NaN NaN 15 14 15 14 14 15 15 12 18 36 74 7630 14 3992 7 46 27 77 19 15 24 35 34 36 4395 13 11 15 380 3 2 4 66 1526:1 7 41397 37494 45300 39107 78333 38638 NaN 54 42 4 2 6 42 15 11 20 17302 74 72 76 2639 12 10 14 56459 20.2 20.5 826 1.5 198 0.4 266 0.5 13 0.0 919 1.6 53640 95.0 149 0 0 1 50.9 32884 57.9
39 47075 Tennessee Haywood 75.3 73.9 76.7 74.0 NaN 75.9 325 488 433 544 525 NaN 480 15 90 51 149 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 15 16 15 14 15 14 11 18 90 597 3870 21 1561 8 NaN NaN 25 20 13 29 39 38 40 1382 13 11 15 139 3 2 5 74 1352:1 NaN 34556 30953 38159 27806 53558 47152 NaN 19 18 NaN NaN NaN 12 13 7 23 4258 60 56 64 974 15 12 18 17573 22.6 18.1 8804 50.1 72 0.4 50 0.3 5 0.0 781 4.4 7716 43.9 94 1 0 1 53.1 8908 47.4
40 47077 Tennessee Henderson 73.4 72.3 74.5 71.3 NaN 73.5 578 565 517 613 720 NaN 556 18 70 41 110 NaN NaN NaN 20 9 6 14 NaN NaN NaN 15 14 15 14 14 15 15 12 19 22 94 4370 16 1233 4 13 16 53 27 20 35 38 37 39 2277 14 12 16 256 4 3 5 76 1321:1 NaN 43806 38310 49302 21176 NaN 46200 NaN 33 31 NaN NaN NaN 22 16 10 24 7831 72 68 76 1091 11 8 14 27751 22.9 18.2 2110 7.6 89 0.3 100 0.4 5 0.0 607 2.2 24364 87.8 87 0 0 1 51.6 21209 76.4
41 47079 Tennessee Henry 74.5 73.5 75.5 71.7 NaN 74.4 696 512 471 553 647 NaN 511 22 82 51 124 NaN NaN NaN 20 9 5 13 NaN NaN NaN 15 15 16 14 14 15 15 12 18 31 113 4700 15 2600 8 11 11 51 23 17 30 35 34 37 2626 15 12 17 263 4 3 5 99 1014:1 NaN 41756 37952 45560 32574 27688 41079 NaN 52 47 6 3 10 43 27 19 36 10173 75 73 78 1277 10 8 12 32450 20.8 22.7 2476 7.6 143 0.4 154 0.5 17 0.1 853 2.6 28299 87.2 107 0 0 1 51.6 21612 66.8
42 47081 Tennessee Hickman 74.4 73.3 75.5 NaN NaN NaN 474 523 474 571 NaN NaN NaN 13 61 33 105 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 15 16 14 14 15 13 9 17 20 97 3430 14 1270 5 17 23 53 31 23 41 36 35 38 2140 15 13 18 254 5 3 6 36 2763:1 13 42824 38330 47318 NaN 34697 40162 NaN 30 22 NaN NaN NaN 14 12 6 19 6998 78 75 82 1068 13 9 16 24864 21.2 17.1 1192 4.8 157 0.6 88 0.4 7 0.0 640 2.6 22457 90.3 60 0 0 1 47.4 24690 100.0
43 47083 Tennessee Houston 74.2 72.3 76.0 NaN NaN NaN 162 522 435 608 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 15 16 15 15 16 14 11 19 NaN NaN 1230 15 116 1 NaN NaN 12 21 11 36 36 35 38 667 14 12 17 76 4 3 6 73 1369:1 NaN 42018 38284 45752 27583 NaN 42586 NaN 52 37 NaN NaN NaN NaN NaN NaN NaN 2315 77 73 81 302 11 6 15 8213 21.8 20.4 236 2.9 31 0.4 48 0.6 6 0.1 191 2.3 7554 92.0 18 0 0 1 50.9 8426 100.0
44 47085 Tennessee Humphreys 73.7 72.3 75.1 NaN NaN NaN 373 525 468 581 NaN NaN NaN 12 75 39 131 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 13 14 13 13 14 14 10 19 12 78 2450 14 491 3 15 27 34 27 18 37 35 34 36 1286 12 10 14 150 4 2 5 81 1232:1 NaN 45866 40144 51588 25759 43110 41172 NaN 40 25 NaN NaN NaN 20 22 13 34 5432 77 73 80 713 11 7 14 18484 21.8 19.7 490 2.7 122 0.7 82 0.4 8 0.0 473 2.6 17098 92.5 13 0 0 1 50.3 15292 82.5
45 47087 Tennessee Jackson 74.6 72.8 76.3 NaN NaN NaN 261 513 444 581 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 17 16 18 16 16 17 15 11 19 13 129 1800 16 1 0 11 32 18 22 13 35 37 35 38 1060 16 13 18 116 5 3 7 9 11677:1 NaN 36815 32535 41095 NaN NaN NaN NaN NaN 58 NaN NaN NaN 10 17 8 32 3470 76 72 80 489 12 8 16 11677 18.2 22.1 56 0.5 77 0.7 17 0.1 4 0.0 248 2.1 11138 95.4 8 0 0 1 50.2 11638 100.0
46 47089 Tennessee Jefferson 75.6 74.8 76.4 NaN 78.9 75.5 946 454 423 485 722 NaN 455 24 55 36 82 NaN NaN NaN 24 7 4 10 NaN NaN NaN 14 13 14 13 13 14 14 10 18 47 103 6420 12 1312 3 22 14 58 16 12 20 33 32 34 4181 13 12 15 372 3 2 4 82 1223:1 NaN 45556 41053 50059 34870 53438 45906 NaN 54 41 4 3 7 26 10 6 14 14751 73 71 76 1878 10 8 12 53804 19.9 19.6 1054 2.0 245 0.5 319 0.6 29 0.1 1971 3.7 49604 92.2 484 1 1 1 50.9 30581 59.5
47 47091 Tennessee Johnson 73.2 71.6 74.9 NaN NaN NaN 396 560 500 620 NaN NaN NaN 14 114 63 192 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 16 17 15 15 16 14 10 17 30 191 2780 16 873 5 NaN NaN 27 22 14 31 37 36 38 1371 15 13 17 121 4 3 5 130 769:1 NaN 36331 31875 40787 70804 21051 33144 NaN 45 19 NaN NaN NaN 20 22 14 35 5305 76 74 79 639 10 7 13 17691 16.8 22.5 403 2.3 53 0.3 41 0.2 10 0.1 379 2.1 16644 94.1 51 0 0 1 46.0 15546 85.2
48 47093 Tennessee Knox 76.7 76.4 77.0 71.8 105.8 76.9 6566 424 414 435 628 200 416 211 55 47 62 103 72 46 217 6 5 7 11 8 5 13 13 14 14 13 14 12 10 13 729 191 59550 13 49168 11 569 42 394 13 11 14 34 33 35 32100 11 10 13 3019 3 2 4 187 535:1 6 55299 53268 57330 29034 36594 56286 NaN 56 42 5 4 6 319 14 13 16 116893 64 63 65 21759 12 12 13 461860 21.2 15.4 40297 8.7 1562 0.3 10754 2.3 521 0.1 19745 4.3 380672 82.4 5461 1 1 1 51.4 47205 10.9
49 47095 Tennessee Lake 71.9 69.9 73.9 81.5 NaN 70.5 172 659 559 760 529 NaN 712 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 16 17 15 14 15 12 9 16 45 666 1690 22 8 0 NaN NaN 10 19 9 34 39 38 40 419 14 12 16 39 4 2 5 121 830:1 NaN 31144 26986 35302 19917 23523 40691 NaN 16 14 NaN NaN NaN NaN NaN NaN NaN 1284 59 56 63 236 12 6 18 7468 14.7 15.6 2139 28.6 35 0.5 14 0.2 2 0.0 171 2.3 4975 66.6 16 0 0 1 36.1 7832 100.0
50 47097 Tennessee Lauderdale 73.1 72.0 74.3 71.4 NaN 73.9 513 559 510 609 600 NaN 545 25 102 66 150 135 NaN 79 27 12 8 18 NaN NaN NaN 17 16 17 15 15 16 17 13 21 84 370 5880 22 1563 6 14 18 54 28 21 37 40 39 41 2076 15 12 17 198 3 2 4 67 1487:1 12 38718 36390 41046 24753 NaN 42131 NaN 41 40 11 7 17 25 19 12 28 5590 57 55 59 1614 18 14 21 25274 23.8 15.7 8377 33.1 186 0.7 199 0.8 3 0.0 622 2.5 15579 61.6 147 1 0 1 50.4 16317 58.7
51 47099 Tennessee Lawrence 74.3 73.5 75.1 NaN NaN NaN 809 520 483 558 NaN NaN NaN 39 91 65 124 NaN NaN NaN 30 8 5 11 NaN NaN NaN 15 14 15 15 14 15 15 11 18 31 89 5760 14 1200 3 25 19 54 18 14 24 37 36 38 3634 15 13 17 413 4 3 5 113 886:1 13 41505 37412 45598 26932 52366 41499 NaN 66 45 3 2 6 29 14 9 20 11887 74 71 76 1641 11 9 13 43396 25.1 17.6 718 1.7 196 0.5 233 0.5 10 0.0 949 2.2 40685 93.8 516 1 0 2 50.9 31769 75.9
52 47101 Tennessee Lewis 74.6 72.8 76.4 NaN NaN NaN 244 532 460 604 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 14 15 14 14 15 15 11 20 9 89 1650 14 221 2 NaN NaN 14 17 9 28 35 33 36 996 15 12 17 102 4 2 5 58 1719:1 NaN 37959 33273 42645 NaN 51927 37441 NaN NaN 27 NaN NaN NaN 24 40 26 60 3674 79 75 82 215 5 2 8 12035 21.4 20.7 234 1.9 59 0.5 68 0.6 6 0.0 271 2.3 11252 93.5 177 2 0 3 51.3 8536 70.2
53 47103 Tennessee Lincoln 74.9 73.9 76.0 71.3 NaN 74.8 604 478 437 519 604 NaN 486 18 60 36 95 NaN NaN NaN 21 8 5 13 NaN NaN NaN 15 14 15 14 14 15 14 11 18 26 92 4370 13 2119 6 16 16 51 22 16 29 39 38 41 2704 14 12 16 333 4 3 6 62 1607:1 NaN 49295 43627 54963 28417 32091 43527 NaN 47 35 NaN NaN NaN 22 13 8 20 9904 73 69 76 1550 12 9 14 33751 22.1 19.2 2285 6.8 280 0.8 167 0.5 54 0.2 1161 3.4 29327 86.9 123 0 0 1 51.0 24183 72.5
54 47105 Tennessee Loudon 76.6 75.7 77.4 NaN 107.2 76.2 935 434 402 466 NaN NaN NaN 25 62 40 91 NaN NaN NaN 24 6 4 9 NaN NaN NaN 13 13 14 13 12 13 13 10 17 49 112 5070 10 5555 11 46 30 52 15 11 19 33 32 34 4263 15 13 17 477 5 3 6 69 1449:1 NaN 57641 52948 62334 30761 44917 56574 NaN 55 42 NaN NaN NaN 31 12 8 17 15282 76 74 78 1770 9 7 11 52152 19.6 25.9 657 1.3 306 0.6 436 0.8 112 0.2 4479 8.6 45853 87.9 1351 3 2 4 50.8 19720 40.6
55 47107 Tennessee Mcminn 74.7 73.9 75.5 72.1 78.4 74.6 1017 508 475 541 647 NaN 512 23 51 32 76 NaN NaN NaN 21 5 3 8 NaN NaN NaN 15 14 15 14 14 15 14 11 17 53 118 7160 14 2657 5 39 25 69 19 15 24 36 35 37 3941 13 11 15 456 4 3 5 91 1102:1 8 39860 36165 43555 21875 25134 41469 NaN 45 32 6 4 9 43 16 12 22 15115 74 73 76 2708 14 11 16 52877 21.3 19.5 1960 3.7 273 0.5 384 0.7 21 0.0 2166 4.1 47146 89.2 283 1 0 1 51.2 31538 60.3
56 47109 Tennessee Mcnairy 73.3 72.2 74.3 78.5 NaN 72.9 587 582 532 632 486 NaN 595 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 16 17 16 15 16 15 12 19 16 73 4160 16 1315 5 20 26 48 26 19 35 36 35 37 2214 15 13 17 224 4 2 5 69 1445:1 15 39407 35013 43801 22290 NaN 35421 NaN 26 17 NaN NaN NaN 28 22 14 31 7428 74 71 76 1008 11 8 14 26004 22.1 20.3 1545 5.9 97 0.4 81 0.3 5 0.0 540 2.1 23372 89.9 52 0 0 1 50.7 22235 85.3
57 47111 Tennessee Macon 74.0 72.8 75.1 NaN NaN NaN 454 543 491 594 NaN NaN NaN 15 65 36 107 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 15 16 15 14 15 15 11 19 10 53 2790 12 412 2 13 18 42 26 19 35 37 36 38 2562 19 16 21 273 5 3 6 58 1720:1 NaN 41266 36222 46310 NaN 28634 36009 NaN NaN 26 NaN NaN NaN 27 23 15 34 6723 73 70 76 992 11 8 15 24079 24.8 15.4 197 0.8 177 0.7 289 1.2 10 0.0 1355 5.6 21929 91.1 309 1 0 2 51.5 17703 79.6
58 47113 Tennessee Madison 75.5 74.9 76.0 72.9 NaN 76.5 1576 466 442 490 588 NaN 416 75 84 66 105 111 NaN 60 80 9 7 11 13 NaN 5 14 14 14 14 13 14 13 11 15 217 266 17900 18 10428 11 39 13 102 15 12 18 37 37 38 6342 11 10 13 841 4 2 5 182 549:1 11 46424 42014 50834 31934 30649 55432 NaN 41 39 12 9 15 89 18 15 22 23511 63 62 64 5991 17 15 19 97643 22.5 16.6 36591 37.5 324 0.3 1115 1.1 46 0.0 3773 3.9 54550 55.9 462 1 0 1 52.7 25386 25.8
59 47115 Tennessee Marion 74.1 73.1 75.1 NaN NaN NaN 641 563 517 610 695 NaN 566 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 14 15 14 14 15 17 13 21 24 99 3990 14 1092 4 18 21 48 24 18 32 37 35 38 2034 12 10 14 213 3 2 4 60 1672:1 NaN 47331 43107 51555 41513 NaN 46903 NaN 41 19 NaN NaN NaN 22 16 10 23 8511 75 72 77 1223 12 9 14 28425 21.3 19.5 1109 3.9 131 0.5 178 0.6 7 0.0 504 1.8 26068 91.7 3 0 0 0 51.1 21747 77.0
60 47117 Tennessee Marshall 75.1 74.1 76.0 70.2 NaN 75.1 570 482 441 522 702 NaN 477 21 70 43 107 NaN NaN NaN 24 10 6 14 NaN NaN NaN 13 13 14 13 13 14 13 9 16 35 133 3840 12 2380 8 29 30 38 17 12 24 35 34 36 2713 14 12 16 341 4 3 6 79 1267:1 NaN 52415 46244 58586 31367 38859 48890 NaN 42 39 6 3 10 21 13 8 20 8608 72 69 75 1260 11 8 13 32931 23.4 15.9 2156 6.5 143 0.4 222 0.7 30 0.1 1693 5.1 28187 85.6 274 1 0 2 50.8 20153 65.8
61 47119 Tennessee Maury 76.2 75.6 76.8 74.4 95.1 76.0 1369 423 399 446 523 238 422 46 55 40 73 NaN NaN NaN 47 6 4 8 NaN NaN NaN 14 13 14 13 13 14 11 9 14 115 158 10880 13 5762 7 51 19 91 15 12 19 36 35 37 6318 12 10 13 866 4 3 5 122 823:1 10 56999 52276 61722 38190 47721 53957 NaN 35 29 4 3 6 60 14 10 18 22891 69 67 70 3790 12 10 13 92163 23.5 15.5 10789 11.7 454 0.5 878 1.0 50 0.1 5383 5.8 73119 79.3 653 1 0 1 51.8 33672 41.6
62 47121 Tennessee Meigs 72.5 70.8 74.2 NaN NaN NaN 291 604 529 678 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 15 16 15 14 15 14 11 19 10 98 1640 14 71 1 18 50 33 40 28 56 35 34 37 1015 15 12 17 117 4 3 6 41 2414:1 NaN 45695 41048 50342 NaN NaN NaN NaN NaN 58 NaN NaN NaN 11 19 9 33 3816 79 76 82 547 12 8 16 12068 20.9 20.7 174 1.4 111 0.9 44 0.4 4 0.0 242 2.0 11383 94.3 43 0 0 1 50.3 11753 100.0
63 47123 Tennessee Monroe 75.2 74.3 76.0 NaN NaN NaN 878 486 451 520 NaN NaN NaN 22 55 35 84 NaN NaN NaN 21 6 4 9 NaN NaN NaN 15 15 16 15 15 16 16 12 20 29 75 5910 13 871 2 43 31 73 23 18 29 35 34 36 4062 16 13 18 437 4 3 6 76 1321:1 15 43749 39208 48290 21481 34034 37748 NaN 56 23 4 2 7 31 14 9 19 13198 76 73 78 1897 11 9 14 46240 21.6 20.7 909 2.0 287 0.6 248 0.5 26 0.1 2123 4.6 41988 90.8 245 1 0 1 50.3 33868 76.1
64 47125 Tennessee Montgomery 76.3 75.9 76.8 75.1 85.2 76.1 2201 425 407 443 490 206 432 128 61 50 71 65 NaN 63 141 6 5 7 9 NaN 6 14 14 15 13 13 14 11 9 13 265 172 27430 15 21355 12 101 17 166 13 11 14 41 40 42 13598 11 10 13 1910 4 2 5 82 1213:1 6 57431 53995 60867 47336 52782 57723 NaN 36 32 7 6 8 172 18 15 21 40631 59 58 60 7969 12 11 13 200182 26.9 9.1 38930 19.4 1462 0.7 4851 2.4 899 0.4 20205 10.1 126915 63.4 1660 1 1 1 50.1 34022 19.7
65 47127 Tennessee Moore 79.0 76.4 81.6 NaN NaN NaN 102 383 303 463 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 12 12 13 13 12 13 15 11 20 NaN NaN 640 10 909 14 NaN NaN NaN NaN NaN NaN 34 33 35 409 11 9 13 52 4 3 5 47 2128:1 NaN 55448 48411 62485 44028 NaN 51535 NaN NaN 33 NaN NaN NaN NaN NaN NaN NaN 2195 84 82 86 251 10 5 15 6384 19.3 21.2 157 2.5 21 0.3 51 0.8 1 0.0 130 2.0 5943 93.1 0 0 0 1 50.1 6354 99.9
66 47129 Tennessee Morgan 73.5 72.1 74.8 NaN NaN NaN 431 558 503 613 NaN NaN NaN 14 82 45 138 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 15 16 15 14 15 13 10 18 21 113 3250 15 1753 8 19 29 28 18 12 27 37 36 38 1651 14 12 17 204 5 3 6 55 1803:1 NaN 40667 36156 45178 NaN 24191 40772 NaN NaN 57 8 4 14 16 15 8 24 5994 81 78 84 794 11 8 14 21636 19.5 17.4 798 3.7 103 0.5 62 0.3 11 0.1 275 1.3 20140 93.1 78 0 0 1 45.3 21962 99.9
67 47131 Tennessee Obion 76.4 75.4 77.4 74.6 NaN 76.2 534 451 411 492 540 NaN 453 16 59 34 97 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 15 16 15 14 15 14 11 18 26 100 5030 16 1553 5 NaN NaN 41 19 14 26 36 35 37 2458 14 12 16 247 4 2 5 135 741:1 9 38160 33894 42426 21760 26250 41715 NaN 54 47 NaN NaN NaN 23 15 10 23 8631 67 66 69 1450 12 10 14 30385 21.9 20.1 3205 10.5 99 0.3 133 0.4 29 0.1 1316 4.3 25211 83.0 238 1 0 1 51.6 19588 61.6
68 47133 Tennessee Overton 74.1 72.9 75.3 NaN NaN NaN 472 537 486 588 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 15 16 15 14 15 14 10 18 15 80 2940 13 98 0 17 26 34 22 15 31 35 33 36 1915 15 13 18 174 4 2 5 68 1467:1 NaN 37154 34533 39775 NaN NaN NaN NaN NaN 32 NaN NaN NaN 29 26 18 38 7028 79 77 81 752 9 6 12 22012 21.6 20.2 121 0.5 85 0.4 76 0.3 2 0.0 312 1.4 21185 96.2 43 0 0 1 50.7 18598 84.2
69 47135 Tennessee Perry 72.1 70.0 74.1 NaN NaN NaN 209 663 565 760 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 16 17 16 15 16 14 10 18 8 120 1260 16 1018 13 NaN NaN 23 42 26 62 35 33 36 644 15 12 17 78 4 3 6 150 665:1 NaN 37135 32246 42024 NaN NaN NaN NaN 40 17 NaN NaN NaN 11 28 14 50 2700 82 78 86 368 12 7 17 7975 21.8 21.2 188 2.4 64 0.8 45 0.6 1 0.0 207 2.6 7312 91.7 4 0 0 1 49.5 7915 100.0
70 47137 Tennessee Pickett 77.6 75.4 79.8 NaN NaN NaN 90 385 301 486 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 14 15 14 14 15 15 11 20 NaN NaN 630 13 14 0 NaN NaN NaN NaN NaN NaN 34 32 35 361 13 11 15 39 4 3 5 59 1691:1 NaN 39858 36179 43537 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1771 81 81 82 85 4 0 9 5073 18.0 27.3 8 0.2 23 0.5 2 0.0 4 0.1 100 2.0 4893 96.5 16 0 0 2 50.1 5077 100.0
71 47139 Tennessee Polk 73.7 72.3 75.1 NaN NaN NaN 393 569 510 629 NaN NaN NaN 13 96 51 164 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 14 15 14 14 15 16 12 21 11 76 2140 13 24 0 13 26 30 26 17 37 36 34 37 1290 13 11 15 151 4 3 6 54 1862:1 NaN 41191 37111 45271 NaN NaN NaN NaN NaN 27 NaN NaN NaN 18 22 13 34 5355 76 73 79 688 10 8 13 16757 19.8 20.7 112 0.7 93 0.6 49 0.3 13 0.1 358 2.1 15920 95.0 42 0 0 1 50.5 16825 100.0
72 47141 Tennessee Putnam 75.5 74.8 76.1 79.4 83.2 75.1 1191 466 438 493 542 217 479 35 55 38 76 NaN NaN NaN 39 6 4 9 NaN NaN NaN 15 14 15 15 14 15 11 8 13 89 140 11170 15 6842 9 41 18 66 13 10 16 33 32 34 6804 15 13 17 717 4 3 6 193 518:1 4 42437 37828 47046 20274 42531 38328 NaN 44 22 3 2 5 63 17 13 21 18527 60 59 62 4562 16 14 18 77674 21.2 16.4 1663 2.1 570 0.7 1137 1.5 113 0.1 4992 6.4 68562 88.3 1641 2 2 3 50.2 25295 35.0
73 47143 Tennessee Rhea 73.5 72.5 74.4 NaN NaN NaN 665 544 500 587 NaN NaN NaN 21 70 44 107 NaN NaN NaN 30 11 8 16 NaN NaN NaN 15 15 16 15 14 15 16 12 20 12 44 5000 15 1666 5 18 18 46 20 15 27 35 34 37 2942 16 14 18 292 4 3 5 73 1362:1 NaN 43426 38137 48715 31974 25139 39841 NaN 39 28 NaN NaN NaN 32 20 13 28 8882 70 67 74 1162 10 7 12 32691 22.7 18.5 712 2.2 169 0.5 176 0.5 18 0.1 1645 5.0 29557 90.4 381 1 1 2 50.5 21635 68.0
74 47145 Tennessee Roane 75.0 74.2 75.8 NaN NaN NaN 1134 524 491 558 454 NaN 533 15 37 21 61 NaN NaN NaN 23 7 5 11 NaN NaN NaN 14 14 14 14 13 14 16 13 21 32 70 6780 13 5134 9 69 43 73 20 15 25 35 34 36 3706 12 10 14 353 3 2 4 89 1128:1 NaN 48391 43598 53184 33176 76446 45311 NaN 37 18 3 2 6 49 19 14 25 16274 75 73 77 2194 11 9 13 53036 19.0 22.5 1383 2.6 238 0.4 336 0.6 30 0.1 1005 1.9 49198 92.8 119 0 0 1 51.2 27628 51.0
75 47147 Tennessee Robertson 75.8 75.1 76.4 70.5 NaN 76.0 1060 435 408 462 675 NaN 425 43 63 46 85 NaN NaN NaN 38 6 4 8 NaN NaN NaN 12 12 13 13 12 13 12 9 15 92 163 6880 10 1073 2 41 20 92 19 16 24 35 34 36 5571 13 11 15 674 4 3 5 87 1150:1 9 59771 55589 63953 33750 51574 60004 NaN 56 43 6 4 9 51 15 11 20 18748 75 73 77 2603 11 9 12 70177 24.3 14.4 5117 7.3 404 0.6 470 0.7 71 0.1 4890 7.0 58539 83.4 899 1 1 2 50.7 35289 53.2
76 47149 Tennessee Rutherford 77.9 77.5 78.2 77.3 93.5 77.7 3244 366 353 378 389 207 375 158 52 44 61 74 53 44 153 6 5 7 7 NaN 6 12 12 13 12 12 12 11 9 14 437 179 34810 12 20893 8 177 19 220 11 9 12 37 36 38 21730 11 10 12 2634 3 2 4 97 1026:1 4 68082 64886 71278 48464 45381 66001 NaN 33 28 4 3 5 153 10 9 12 69934 66 65 67 11847 11 10 12 317157 24.7 10.1 47505 15.0 1784 0.6 11175 3.5 354 0.1 25494 8.0 224712 70.9 6209 2 2 3 50.7 44699 17.0
77 47151 Tennessee Scott 72.2 70.9 73.4 NaN NaN NaN 467 609 552 666 NaN NaN NaN 18 84 50 132 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 16 16 17 16 15 16 12 9 17 7 39 3800 17 382 2 13 20 29 19 13 27 35 33 36 1840 14 12 17 192 4 2 5 105 956:1 NaN 34828 30720 38936 NaN 20625 32460 NaN NaN 35 NaN NaN NaN 30 27 18 39 5941 70 67 73 1073 13 10 17 21989 24.4 16.4 44 0.2 68 0.3 53 0.2 4 0.0 203 0.9 21401 97.3 17 0 0 1 50.7 17906 80.6
78 47153 Tennessee Sequatchie 76.9 75.4 78.4 NaN NaN NaN 252 418 363 473 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 13 14 14 13 14 14 10 18 6 48 1800 12 3 0 NaN NaN 12 12 6 20 35 33 36 1120 13 11 15 114 3 2 5 81 1228:1 NaN 45100 39345 50855 NaN 58750 50772 NaN NaN 9 NaN NaN NaN 11 15 7 27 4153 75 71 80 459 9 6 12 14736 21.1 19.9 83 0.6 99 0.7 69 0.5 11 0.1 537 3.6 13779 93.5 56 0 0 1 50.6 10415 73.8
79 47155 Tennessee Sevier 76.3 75.7 76.8 NaN 87.4 75.9 1638 437 415 460 NaN 251 452 33 41 28 57 NaN NaN NaN 41 6 4 8 NaN NaN NaN 14 14 14 14 13 14 15 11 19 63 77 11300 12 4885 5 85 29 119 18 15 21 35 33 36 11021 19 17 21 966 5 3 6 112 896:1 5 48482 45130 51834 44080 32334 45486 NaN 64 22 NaN NaN NaN 66 14 11 18 24861 67 66 69 4133 12 10 13 97638 20.8 19.2 1084 1.1 595 0.6 1315 1.3 43 0.0 5876 6.0 87817 89.9 1819 2 2 3 51.0 50920 56.6
80 47157 Tennessee Shelby 75.8 75.6 76.0 73.5 91.9 78.0 13893 465 458 473 572 223 374 753 79 74 85 102 48 46 912 10 9 10 13 4 5 14 14 14 14 14 14 12 11 14 5731 750 198610 21 104909 11 600 21 838 13 12 14 38 38 39 84327 15 14 16 8925 4 3 5 122 820:1 11 49563 47605 51521 35061 36912 72343 NaN 65 61 19 18 20 1107 24 22 25 195204 56 55 56 60300 18 17 19 936961 25.0 13.1 502118 53.6 2988 0.3 25194 2.7 739 0.1 59571 6.4 335966 35.9 17727 2 2 2 52.4 25601 2.8
81 47159 Tennessee Smith 76.4 75.2 77.6 NaN NaN NaN 321 447 396 498 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 13 14 14 13 14 14 11 19 18 112 2330 12 110 1 22 38 33 24 17 34 33 32 35 1498 13 11 15 184 4 3 5 51 1964:1 NaN 47653 41674 53632 21797 46500 45023 NaN 42 23 NaN NaN NaN 13 14 7 23 5633 75 72 77 712 10 7 13 19636 23.0 16.6 429 2.2 95 0.5 73 0.4 3 0.0 523 2.7 18272 93.1 16 0 0 1 50.3 15884 82.9
82 47161 Tennessee Stewart 75.7 74.0 77.4 NaN NaN NaN 240 452 391 513 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 13 14 13 13 14 15 11 20 8 71 1840 14 97 1 NaN NaN 18 19 12 31 35 34 36 1051 13 11 16 139 5 3 7 82 1214:1 NaN 48175 42038 54312 32448 NaN 45066 NaN 26 21 NaN NaN NaN 20 30 18 47 3769 71 67 75 603 12 8 16 13355 20.8 19.9 223 1.7 100 0.7 139 1.0 7 0.1 379 2.8 12279 91.9 23 0 0 1 50.0 13324 100.0
83 47163 Tennessee Sullivan 75.8 75.4 76.2 73.7 103.7 75.7 2934 471 453 489 628 NaN 475 53 43 32 56 NaN NaN NaN 65 6 5 8 NaN NaN NaN 14 13 14 14 14 15 15 13 17 121 90 20220 13 20210 13 120 25 156 14 12 16 35 34 36 11261 12 11 14 908 3 2 4 183 546:1 8 45224 41766 48682 30486 25714 42876 NaN 54 35 3 2 4 112 14 12 17 48178 73 71 74 6306 10 9 11 157158 19.5 21.5 3423 2.2 566 0.4 1212 0.8 67 0.0 2936 1.9 146947 93.5 586 0 0 1 51.4 40086 25.6
84 47165 Tennessee Sumner 78.0 77.6 78.4 77.5 88.0 77.7 2320 363 348 378 410 163 369 74 43 34 54 NaN NaN NaN 81 6 5 7 NaN NaN NaN 12 12 12 12 12 13 11 9 14 180 123 17550 10 11149 7 103 19 152 13 11 15 33 32 34 11950 11 10 12 1448 3 2 4 89 1119:1 6 65174 62008 68340 54928 39959 62483 NaN 45 38 4 3 5 142 16 14 19 47485 74 72 75 6688 11 10 12 183545 23.8 15.4 13623 7.4 706 0.4 2599 1.4 191 0.1 9034 4.9 154733 84.3 1495 1 1 1 51.1 44792 27.9
85 47167 Tennessee Tipton 74.7 74.0 75.4 73.3 NaN 74.7 1005 484 453 514 539 NaN 485 32 52 35 73 92 NaN 43 30 6 4 8 NaN NaN NaN 14 13 14 13 13 13 15 12 18 81 160 8770 14 3742 6 49 27 81 19 15 23 37 36 38 4732 13 11 15 480 3 2 4 70 1427:1 10 56511 50916 62106 32392 74095 61702 NaN 41 35 6 4 9 54 18 13 23 14831 69 67 72 2133 10 8 12 61366 24.9 14.1 11263 18.4 293 0.5 466 0.8 97 0.2 1730 2.8 46492 75.8 391 1 0 1 50.7 33671 55.1
86 47169 Tennessee Trousdale 74.1 72.3 75.8 NaN NaN NaN 153 499 418 580 658 NaN 495 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 13 13 14 13 13 14 14 10 19 15 223 1010 13 NaN NaN NaN NaN 16 28 16 45 35 34 36 769 15 13 18 86 4 3 6 99 1008:1 NaN 49711 45455 53967 27102 NaN 49023 NaN 58 57 NaN NaN NaN 10 24 11 44 2101 71 65 78 398 14 8 21 10083 19.1 13.8 1066 10.6 69 0.7 56 0.6 1 0.0 266 2.6 8481 84.1 52 1 0 2 43.5 7870 100.0
87 47171 Tennessee Unicoi 74.4 73.0 75.7 NaN NaN NaN 392 531 474 587 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 14 15 15 14 15 15 11 19 15 97 2580 14 1124 6 20 37 23 18 12 27 35 34 36 1465 14 12 16 130 4 2 5 56 1776:1 NaN 41188 36627 45749 NaN NaN NaN NaN NaN 16 8 4 15 21 24 15 36 5513 72 69 76 803 11 8 15 17759 18.8 22.9 74 0.4 73 0.4 45 0.3 8 0.0 854 4.8 16557 93.2 211 1 0 2 50.8 8180 44.7
88 47173 Tennessee Union 74.1 72.8 75.5 NaN NaN NaN 388 542 485 599 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 15 16 15 14 15 16 11 21 NaN NaN 2760 15 139 1 26 45 26 19 13 28 35 34 36 1781 16 13 18 190 4 3 6 51 1944:1 NaN 40357 35994 44720 NaN NaN NaN NaN NaN 19 8 4 15 26 27 18 40 5492 76 72 79 638 9 6 12 19442 22.0 18.0 56 0.3 88 0.5 41 0.2 16 0.1 315 1.6 18736 96.4 26 0 0 1 50.6 19109 100.0
89 47175 Tennessee Van buren 73.4 70.9 76.0 NaN NaN NaN 133 548 445 651 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 14 15 14 14 15 14 10 19 NaN NaN 700 13 20 0 NaN NaN 11 28 14 50 34 33 36 465 14 12 17 48 4 3 6 35 2871:1 NaN 38478 33509 43447 NaN NaN NaN NaN NaN 13 NaN NaN NaN NaN NaN NaN NaN 1888 88 87 88 192 9 5 14 5742 19.4 23.2 43 0.7 22 0.4 12 0.2 1 0.0 88 1.5 5506 95.9 0 0 0 2 49.6 5548 100.0
90 47177 Tennessee Warren 74.4 73.5 75.2 NaN 79.4 74.1 781 526 487 564 642 NaN 540 22 57 36 87 NaN NaN NaN 27 8 6 12 NaN NaN NaN 15 15 16 15 14 15 17 13 21 31 93 5140 13 1607 4 21 17 87 31 25 38 34 33 35 4082 17 15 20 441 4 3 6 101 991:1 11 37692 34361 41023 25990 37145 37251 NaN 29 19 5 3 8 37 18 13 25 10794 69 66 71 1582 11 9 13 40651 23.7 17.4 1308 3.2 203 0.5 318 0.8 36 0.1 3707 9.1 34637 85.2 835 2 1 3 50.7 24453 61.4
91 47179 Tennessee Washington 76.0 75.5 76.4 71.9 95.0 75.9 2107 453 433 474 694 283 451 72 73 57 92 NaN NaN NaN 84 9 7 11 NaN NaN NaN 14 13 14 14 13 14 14 12 17 186 171 16860 13 9478 8 100 26 78 9 7 11 38 37 39 9218 12 10 14 822 3 2 4 293 341:1 4 43194 39392 46996 29529 30500 45091 NaN 50 37 3 2 4 76 12 10 15 34063 65 63 66 5974 12 10 13 127806 19.2 17.9 5365 4.2 554 0.4 1982 1.6 75 0.1 4412 3.5 113428 88.8 312 0 0 0 51.1 32493 26.4
92 47181 Tennessee Wayne 75.6 74.3 76.9 NaN NaN NaN 335 509 452 566 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 15 16 14 14 15 14 11 19 14 95 2330 14 1556 9 NaN NaN 21 18 11 27 38 36 39 1271 15 12 17 145 5 3 7 90 1106:1 17 36612 32025 41199 40096 41313 34696 NaN NaN 32 NaN NaN NaN 12 14 7 25 4715 80 77 84 586 11 8 14 16583 17.3 19.1 1051 6.3 64 0.4 50 0.3 5 0.0 345 2.1 14925 90.0 28 0 0 1 44.9 17021 100.0
93 47183 Tennessee Weakley 75.7 74.6 76.7 73.5 NaN 75.7 550 470 429 511 530 NaN 467 19 72 43 113 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 14 14 15 14 14 15 12 9 15 16 55 5160 15 2517 7 15 15 40 17 12 23 35 34 36 2293 12 10 14 232 3 2 5 93 1075:1 NaN 39424 34790 44058 19965 31250 38270 NaN 49 46 6 3 10 36 21 15 29 9060 67 65 69 1401 11 9 13 33337 19.5 18.3 2538 7.6 136 0.4 394 1.2 14 0.0 817 2.5 29030 87.1 131 0 0 1 51.1 23466 67.0
94 47185 Tennessee White 74.2 73.2 75.3 NaN NaN NaN 553 539 491 586 NaN NaN NaN 13 56 30 95 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 14 15 14 14 15 15 11 20 22 99 3350 13 1409 5 21 26 47 25 19 34 34 33 35 2140 14 12 16 201 3 2 5 71 1408:1 NaN 39642 34857 44427 30779 30781 38331 NaN 62 47 NaN NaN NaN 29 22 15 31 7673 78 76 81 942 10 7 13 26753 21.8 20.1 460 1.7 134 0.5 102 0.4 24 0.1 686 2.6 25005 93.5 80 0 0 1 51.1 20201 78.2
95 47187 Tennessee Williamson 82.1 81.7 82.5 80.2 87.2 81.9 1475 206 196 217 299 178 208 59 25 19 32 NaN NaN NaN 54 4 3 5 NaN NaN NaN 10 10 10 11 10 11 10 8 13 137 80 14450 7 6068 3 81 12 99 7 6 8 33 32 34 9314 7 6 8 1864 3 2 4 110 905:1 3 111427 102231 120623 70818 69550 105715 NaN 30 30 1 1 2 95 9 7 11 58932 81 80 81 6192 9 8 10 226257 27.5 12.8 9783 4.3 601 0.3 10012 4.4 132 0.1 10857 4.8 191775 84.8 1459 1 1 1 51.1 35512 19.4
96 47189 Tennessee Wilson 78.4 77.9 78.9 75.4 98.8 78.4 1664 347 330 365 443 187 345 61 49 37 62 112 NaN 43 45 5 3 6 NaN NaN NaN 11 11 11 12 11 12 13 10 16 134 125 12130 10 4430 4 105 26 110 12 10 15 33 32 34 7603 9 8 11 1009 3 2 4 111 898:1 5 70674 65885 75463 51448 58148 67758 NaN 32 25 3 2 5 83 13 10 16 36338 77 75 79 4861 11 9 12 136442 23.9 15.4 9388 6.9 685 0.5 2225 1.6 126 0.1 5757 4.2 116243 85.2 924 1 0 1 50.9 43850 38.5
In [104]:
sns.pairplot((abs_health_3.dropna()))
Out[104]:
<seaborn.axisgrid.PairGrid at 0x2733f647748>
In [178]:
more_disp = more_disp.drop('County',axis=1)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-178-15cbee0a744b> in <module>()
----> 1 more_disp = more_disp.drop('County',axis=1)

~\Anaconda3\lib\site-packages\pandas\core\frame.py in drop(self, labels, axis, index, columns, level, inplace, errors)
   3692                                            index=index, columns=columns,
   3693                                            level=level, inplace=inplace,
-> 3694                                            errors=errors)
   3695 
   3696     @rewrite_axis_style_signature('mapper', [('copy', True),

~\Anaconda3\lib\site-packages\pandas\core\generic.py in drop(self, labels, axis, index, columns, level, inplace, errors)
   3106         for axis, labels in axes.items():
   3107             if labels is not None:
-> 3108                 obj = obj._drop_axis(labels, axis, level=level, errors=errors)
   3109 
   3110         if inplace:

~\Anaconda3\lib\site-packages\pandas\core\generic.py in _drop_axis(self, labels, axis, level, errors)
   3138                 new_axis = axis.drop(labels, level=level, errors=errors)
   3139             else:
-> 3140                 new_axis = axis.drop(labels, errors=errors)
   3141             dropped = self.reindex(**{axis_name: new_axis})
   3142             try:

~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in drop(self, labels, errors)
   4385             if errors != 'ignore':
   4386                 raise KeyError(
-> 4387                     'labels %s not contained in axis' % labels[mask])
   4388             indexer = indexer[~mask]
   4389         return self.delete(indexer)

KeyError: "labels ['County'] not contained in axis"
In [179]:
more_dispa = more_disp.iloc[:,1:].astype(float)
more_dispa['County'] = more_disp['NAME10']
In [185]:
more_dispa = more_dispa.drop('%Children eligible for free or reduced price lunch', axis = 1)
In [186]:
corr = more_dispa.corr()

# plot the heatmap
sns.heatmap(corr)
Out[186]:
<matplotlib.axes._subplots.AxesSubplot at 0x2736d7fdf60>
In [187]:
more_disp.shape
Out[187]:
(95, 36)
In [189]:
abs_health3.shape
Out[189]:
(95, 186)

Model

In [302]:
from sklearn.linear_model import LinearRegression
from sklearn.utils import resample
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.cross_validation import train_test_split
In [211]:
more_dispa.shape
Out[211]:
(95, 35)
In [215]:
p1 = more_dispa.iloc[:,3:35]
In [236]:
p2 = abs_health_3a.iloc[:, 4:]#change to 4?
In [238]:
prep = pd.merge(p1,p2, on = 'County')
In [246]:
X = prep.drop(['County','Child mortality # Deaths','Infant mortality # of Deaths', 'HIV prevalence # of cases',
              '# of Food insecure', '# with Limited access to healthy foods', '# of Drug overdose deaths', '# of Uninsured adults',
              '# of Uninsured children','# of Homeowners'],axis = 1)
In [294]:
X.shape
Out[294]:
(95, 39)
In [221]:
y = abs_health_3a.iloc[:,0]
In [249]:
X=X.fillna(X.mean())
In [250]:
X.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 95 entries, 0 to 94
Data columns (total 39 columns):
classroom_teacher                                  95 non-null float64
principal                                          95 non-null float64
Life expectancy                                    95 non-null float64
Premature age-adjusted mortality                   95 non-null float64
Child Mortality Rate                               95 non-null float64
Infant Mortality Rate                              95 non-null float64
% Frequent physical distress                       95 non-null float64
%Frequent mental distress                          95 non-null float64
% Diabetes prevalence                              95 non-null float64
HIV Prevalence Rate                                95 non-null float64
% of Food Insecure                                 95 non-null float64
% of limited access to healthy foods               95 non-null float64
Drug Overdose Mortality Rate                       95 non-null float64
% Insufficient sleep                               95 non-null float64
% of Uninsured Adults                              95 non-null float64
% of Uninsured children                            95 non-null float64
Median household income                            95 non-null float64
Homicides                                          95 non-null float64
# of Firearm fatalities                            95 non-null float64
% of Homeowners                                    95 non-null float64
# of Households with Severe housing cost burden    95 non-null float64
% of Severe Housing Cost Burden                    95 non-null float64
Poor mental health days                            95 non-null float64
% Low Birth Weight                                 95 non-null float64
Adult smoking                                      95 non-null float64
Adult obesity                                      95 non-null float64
Food environment index                             95 non-null float64
Physical inactivity                                95 non-null float64
Teen births                                        95 non-null float64
% Uninsured                                        95 non-null float64
Primary care physicians                            95 non-null float64
Dentists                                           95 non-null float64
Mental health providers                            95 non-null float64
Flu vaccinations                                   95 non-null float64
% Unemployed                                       95 non-null float64
Children in poverty                                95 non-null float64
% Single-Parent Household                          95 non-null float64
Violent crime                                      95 non-null float64
Severe housing problems                            95 non-null float64
dtypes: float64(39)
memory usage: 29.7 KB
In [298]:
Xtrain, Xtest,ytrain, ytest = train_test_split(X,y,test_size = 0.25, random_state=42)
In [309]:
linear_model = LinearRegression()
linear_model.fit(Xtrain, ytrain)
linear_predict = tn_ab_model.predict(Xtest)
In [262]:
linear_model.coef_[0]
Out[262]:
3.1653027140779854e-06
In [348]:
linear_model.intercept_
Out[348]:
0.49259947327922404
In [316]:
y_try=np.array(ytest)
In [351]:
df=pd.DataFrame(y_try,linear_predict).reset_index()
df.columns=('Linear_predict','y_actual')
In [356]:
df[['y_actual','Linear_predict']].plot(alpha=0.5)
Out[356]:
[Text(140.375,0.5,'help')]
In [288]:
params = pd.Series(linear_model.coef_,index=X.columns)
In [292]:
np.random.seed(1)
err = np.std([linear_model.fit(*resample(X,y)).coef_
             for i in range(1000)],0)

print(pd.DataFrame({'effect':params,
                     'error': err}))

                                                       effect     error
classroom_teacher                                3.165303e-06  0.000006
principal                                        1.097783e-06  0.000002
Life expectancy                                  1.587947e-03  0.011374
Premature age-adjusted mortality                -4.089742e-05  0.000088
Child Mortality Rate                            -8.587819e-04  0.001218
Infant Mortality Rate                            1.582024e-02  0.011118
% Frequent physical distress                    -2.587153e-02  0.025625
%Frequent mental distress                       -2.770411e-02  0.024326
% Diabetes prevalence                           -4.987517e-03  0.009763
HIV Prevalence Rate                              1.735406e-05  0.000178
% of Food Insecure                              -1.979509e-02  0.030013
% of limited access to healthy foods            -9.873472e-03  0.018790
Drug Overdose Mortality Rate                     1.429169e-03  0.001275
% Insufficient sleep                            -2.945012e-03  0.008342
% of Uninsured Adults                            3.867257e-03  0.022186
% of Uninsured children                          6.003258e-03  0.028345
Median household income                          2.813301e-07  0.000003
Homicides                                       -5.845238e-03  0.008310
# of Firearm fatalities                          7.078335e-04  0.001076
% of Homeowners                                  7.483313e-04  0.003311
# of Households with Severe housing cost burden -3.432733e-06  0.000038
% of Severe Housing Cost Burden                  2.366836e-03  0.010130
Poor mental health days                          1.533780e-01  0.130880
% Low Birth Weight                               2.012967e-03  0.012526
Adult smoking                                    6.354654e-03  0.008389
Adult obesity                                    8.538415e-03  0.005825
Food environment index                          -1.091789e-01  0.193318
Physical inactivity                             -8.819751e-03  0.004175
Teen births                                     -7.969505e-04  0.001959
% Uninsured                                      1.351271e-04  0.029050
Primary care physicians                          7.059433e-04  0.000658
Dentists                                        -1.890640e-03  0.001358
Mental health providers                         -1.195661e-04  0.000267
Flu vaccinations                                 2.776199e-03  0.002465
% Unemployed                                     1.795528e-02  0.022178
Children in poverty                              5.978580e-03  0.004665
% Single-Parent Household                       -1.179948e-03  0.002816
Violent crime                                    1.400402e-04  0.000123
Severe housing problems                         -3.034072e-04  0.008896
In [336]:
param_error = pd.DataFrame({'effect':params,
                     'error': err})
In [339]:
param_error.sort_values(by='effect', ascending = False)
Out[339]:
effect error
Poor mental health days 1.533780e-01 0.130880
% Unemployed 1.795528e-02 0.022178
Infant Mortality Rate 1.582024e-02 0.011118
Adult obesity 8.538415e-03 0.005825
Adult smoking 6.354654e-03 0.008389
% of Uninsured children 6.003258e-03 0.028345
Children in poverty 5.978580e-03 0.004665
% of Uninsured Adults 3.867257e-03 0.022186
Flu vaccinations 2.776199e-03 0.002465
% of Severe Housing Cost Burden 2.366836e-03 0.010130
% Low Birth Weight 2.012967e-03 0.012526
Life expectancy 1.587947e-03 0.011374
Drug Overdose Mortality Rate 1.429169e-03 0.001275
% of Homeowners 7.483313e-04 0.003311
# of Firearm fatalities 7.078335e-04 0.001076
Primary care physicians 7.059433e-04 0.000658
Violent crime 1.400402e-04 0.000123
% Uninsured 1.351271e-04 0.029050
HIV Prevalence Rate 1.735406e-05 0.000178
classroom_teacher 3.165303e-06 0.000006
principal 1.097783e-06 0.000002
Median household income 2.813301e-07 0.000003
# of Households with Severe housing cost burden -3.432733e-06 0.000038
Premature age-adjusted mortality -4.089742e-05 0.000088
Mental health providers -1.195661e-04 0.000267
Severe housing problems -3.034072e-04 0.008896
Teen births -7.969505e-04 0.001959
Child Mortality Rate -8.587819e-04 0.001218
% Single-Parent Household -1.179948e-03 0.002816
Dentists -1.890640e-03 0.001358
% Insufficient sleep -2.945012e-03 0.008342
% Diabetes prevalence -4.987517e-03 0.009763
Homicides -5.845238e-03 0.008310
Physical inactivity -8.819751e-03 0.004175
% of limited access to healthy foods -9.873472e-03 0.018790
% of Food Insecure -1.979509e-02 0.030013
% Frequent physical distress -2.587153e-02 0.025625
%Frequent mental distress -2.770411e-02 0.024326
Food environment index -1.091789e-01 0.193318
In [327]:
forest = RandomForestRegressor()
forest.fit(Xtrain,ytrain)

RF_model = forest.predict(Xtest)
In [331]:
df['RF model'] = RF_model
In [334]:
df[['ytest','Linear model','RF model']].plot(alpha=0.5)
plt.xlabel = 'Observations'
plt.ylabel = 'Percentage of Chronically Absent'
plt.show()
In [345]:
from sklearn.metrics import mean_squared_error, mean_absolute_error
from math import sqrt

MSE_linear = mean_squared_error(ytest,linear_predict)
MSE_rf = mean_squared_error(ytest, RF_model)

rms_linear = sqrt(mean_squared_error(ytest, linear_predict)) #May not be ideal because of outlier
rms_rf = sqrt(mean_squared_error(ytest,RF_model))

MAE_linear = mean_absolute_error(ytest,linear_predict)
MAE_rf = mean_absolute_error(ytest,RF_model)

print('The mean square error (MSE) for the linear model was', MSE_linear)
print('The mean square error (MSE) for the random forest model was', MSE_rf)
print('The root mean square error (RMSE) for the linear model was', rms_linear)
print('The root mean square error (RMSE) for the random forest model was', rms_rf)
print('The mean absolute error (MAE) for the linear model was', MAE_linear)
print('The mean absolute error (MAE) for the random forest model was', MAE_rf)

The mean square error (MSE) for the linear model was 0.004449960491814895
The mean square error (MSE) for the random forest model was 0.003920884483173963
The root mean square error (RMSE) for the linear model was 0.06670802419360729
The root mean square error (RMSE) for the random forest model was 0.06261696641625146
The mean absolute error (MAE) for the linear model was 0.04804978027775752
The mean absolute error (MAE) for the random forest model was 0.04440538858088191